| 1. |
- Abbaspour Asadollah, Sara, et al.
(författare)
-
Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
-
Ingår i: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
-
Tidskriftsartikel (refereegranskat)abstract
- Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. [Figure not available: see fulltext.].
|
|
| 2. |
- Abbaspour, Sara, et al.
(författare)
-
A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
- 2016
-
Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier BV. - 1050-6411 .- 1873-5711. ; 26, s. 52-59
-
Tidskriftsartikel (refereegranskat)abstract
- In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).
|
|
| 3. |
- Abbaspour, Sara, 1984-, et al.
(författare)
-
ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
- 2015
-
Ingår i: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 91-97
-
Konferensbidrag (refereegranskat)abstract
- This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.
|
|
| 4. |
- Abbaspour, Sara, et al.
(författare)
-
Evaluation of wavelet based methods in removing motion artifact from ECG signal
- 2015
-
Ingår i: IFMBE Proceedings. - Cham : Springer International Publishing. - 9783319129662 ; , s. 1-4
-
Konferensbidrag (refereegranskat)abstract
- Accurate recording and precise analysis of the electrocardiogram (ECG) signals are crucial in the pathophysiological study and clinical treatment. These recordings are often corrupted by different artifacts. The aim of this study is to propose two different methods, wavelet transform based on nonlinear thresholding and a combination method using wavelet and independent component analysis (ICA), to remove motion artifact from ECG signals. To evaluate the performance of the proposed methods, the developed techniques are applied to the real and simulated ECG data. The results of this evaluation are presented using quantitative and qualitative criteria. The results show that the proposed methods are able to reduce motion artifacts in ECG signals. Signal to noise ratio (SNR) of the wavelet technique is equal to 13.85. The wavelet-ICA method performed better with SNR of 14.23.
|
|
| 5. |
- Afifi, S., et al.
(författare)
-
A Novel Medical Device for Early Detection of Melanoma
- 2019
-
Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 122-127
-
Tidskriftsartikel (refereegranskat)abstract
- Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.
|
|
| 6. |
- Baig, M. M., et al.
(författare)
-
A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption
- 2017
-
Ingår i: Journal of medical systems. - : Springer New York LLC. - 0148-5598 .- 1573-689X. ; 41:7
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.
|
|
| 7. |
- Baig, M. M., et al.
(författare)
-
Advanced decision support system for older adults
- 2015
-
Ingår i: Studies in Health Technology and Informatics, vol. 211. - 9781614995159 ; , s. 235-240
-
Konferensbidrag (refereegranskat)abstract
- Decision support systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia and to generate early warnings.
|
|
| 8. |
- Baig, Mirza Mansoor, et al.
(författare)
-
Clinical decision support systems in hospital care using ubiquitous devices : Current issues and challenges
- 2019
-
Ingår i: Health Informatics Journal. - : SAGE PUBLICATIONS INC. - 1460-4582 .- 1741-2811. ; 25:3, s. 1091-1104
-
Tidskriftsartikel (refereegranskat)abstract
- Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.
|
|
| 9. |
- Baig, M.M., et al.
(författare)
-
Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care
- 2015
-
Ingår i: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). - 9781424492701 ; , s. 1215-1218
-
Konferensbidrag (refereegranskat)abstract
- Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet computers which are increasingly integrating into the healthcare mainstream as clinical decision support systems. Despite the benefits of table-based healthcare applications, there are concerns around the accuracy, security and stability of such applications. In this study, we developed five tablet-based application screens for remote patient monitoring at hospital care settings and identified related issues and challenges. The ultimate aim of this research is to integrate decision support algorithms into the monitoring system in order to improve inpatient care and the effectiveness of such applications.
|
|
| 10. |
- GholamHosseini, Hamid, et al.
(författare)
-
A multifactorial falls risk prediction model for hospitalized older adults
- 2014
-
Ingår i: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. - 9781424479290 ; , s. 3484-3487
-
Konferensbidrag (refereegranskat)abstract
- Ageing population worldwide has grown fast with more cases of chronic illnesses and co-morbidity, involving higher healthcare costs. Falls are one of the leading causes of unintentional injury-related deaths in older adults. The aim of this study was to develop a robust multifactorial model toward the falls risk prediction. The proposed model employs real-time vital signs, motion data, falls history and muscle strength. Moreover, it identifies high-risk individuals for the development falls in their activity of daily living (ADL). The falls risk prediction model has been tested at a controlled-environment in hospital with 30 patients and compared with the results from the Morse fall scale. The simulated results show the proposed algorithm achieved an accuracy of 98%, sensitivity of 96% and specificity of 100% among a total of 80 intentional falls and 40 ADLs. The ultimate aim of this study is to extend the application to elderly home care and monitoring.
|
|
| 11. |
- GholamHosseini, Hamid, et al.
(författare)
-
A Smartphone-based Obesity Risk Assessment Application Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile
- 2020
-
Ingår i: European Journal of Biomedical Informatics. ; 16:2, s. 1-10
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives: There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of healthcare. Methods: This research focuses on developing a mobile application for obesity risk assessment using wearable technology and proposing an individualized activity/dietary plan. From calculating the Body Mass Index, we established an individualized health profile and used the average data collected by a smart vest to offer the level of activity and health goals. Results: We developed an algorithm to assess the risk of obesity using the individual’s current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect data from the wearable vest and user-reported data. Based on the collected data, the proposed application assessed the risk of obesity/ overweight, measured the current activity level and recommended an optimized calorie plan. Conclusion: The proposed model can integrate data from multiple sources including sensors, wearable garment, medical devices and also the manually entered (user reported) data. The model (and its rule-based engine) will continuously self-learn and tune the model for better accuracy and reliability over-time.
|
|
| 12. |
|
|
| 13. |
- GholamHosseini, Hamid, et al.
(författare)
-
Cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio
- 2018
-
Ingår i: Studies in Health Technology and Informatics, vol 249. - : IOS Press. - 9781614998679 ; , s. 77-83
-
Konferensbidrag (refereegranskat)abstract
- High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multiparameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP.
|
|
| 14. |
|
|
| 15. |
- GholamHosseini, Hamid, et al.
(författare)
-
Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile
- 2019
-
Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 91-96
-
Tidskriftsartikel (refereegranskat)abstract
- There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.
|
|
| 16. |
- GholamHosseini, Hamid, et al.
(författare)
-
Smartphone-based Blood Pressure Monitoring for Falls Risk Assessment
- 2017
-
Ingår i: Human Monitoring, Smart Health and Assisted Living: Techniques and Technologies. - United Kingdom : IET Publishing. - 9781785611513 ; , s. 203-215
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Smart patient monitoring systems have rapidly evolved during the past two decades and have the potential to improve current patient care and medical staffworkflow. With advanced sensors, sophisticated hardware and fast-growing wireless communication technologies, there are enormous opportunities for ubiquitous solutions in all areas of healthcare, especially patient monitoring. Current methods of non-invasive blood pressure measurement are based on inflation and deflation of a cuff with some effects on arteries where blood pressure is being measured. This approach is non-continuous, time delayed, and might cause patient discomfort. We aim to monitor and measure cuff-less and continuous blood pressure using a smartphone. Cuff-less approach enables continuous blood pressure monitoring capabilities and is particularly attractive as blood pressure is one of the most important factors to assess risk of falls in older adults. A smartphone application was developed to collect PhotoPlethysmoGram (PPG) waveform and electrocardiogram (ECG) in order to calculate pulse transit time (PTT). The user's systolic blood pressure is calculated using the PPT and precise optimisation model. The proposed application can be integrated with our developed falls risk assessment algorithm for inpatient older adults. This study proposes a novel approach of continuous blood pressure monitoring using cuff-less method that can be employed for prevention of inpatient falls using smartphone.
|
|
| 17. |
- GholamHosseini, Hamid, et al.
(författare)
-
Smartphone-based continuous blood pressure measurement using pulse transit time
- 2016
-
Ingår i: Studies in Health Technology and Informatics. - 9781614996521 ; , s. 84-89
-
Konferensbidrag (refereegranskat)abstract
- The increasing availability of low cost and easy to use personalized medical monitoring devices has opened the door for new and innovative methods of health monitoring to emerge. Cuff-less and continuous methods of measuring blood pressure are particularly attractive as blood pressure is one of the most important measurements of long term cardiovascular health. Current methods of noninvasive blood pressure measurement are based on inflation and deflation of a cuff with some effects on arteries where blood pressure is being measured. This inflation can also cause patient discomfort and alter the measurement results. In this work, a mobile application was developed to collate the PhotoPlethysmoGramm (PPG) waveform provided by a pulse oximeter and the electrocardiogram (ECG) for calculating the pulse transit time. This information is then indirectly related to the user's systolic blood pressure. The developed application successfully connects to the PPG and ECG monitoring devices using Bluetooth wireless connection and stores the data onto an online server. The pulse transit time is estimated in real time and the user's systolic blood pressure can be estimated after the system has been calibrated. The synchronization between the two devices was found to pose a challenge to this method of continuous blood pressure monitoring. However, the implemented continuous blood pressure monitoring system effectively serves as a proof of concept. This combined with the massive benefits that an accurate and robust continuous blood pressure monitoring system would provide indicates that it is certainly worthwhile to further develop this system.
|
|
| 18. |
- Mansoor Baig, Mirza, et al.
(författare)
-
Review of Vital Signs Monitoring Systems - – Patient’´s Acceptability, Issues and Challenges
- 2014
-
Ingår i: Neuroscience and Biomedical Engineering. - : Bentham Science Publishers Ltd.. - 2213-3852. ; 2:1, s. 2-13
-
Tidskriftsartikel (refereegranskat)abstract
- Vital signs are often considered as critical information to assess initial health condition and underlying health issues. Vital signs can contribute towards early detection, early diagnosis and risk reduction of fatal incidents. Today’s advanced monitoring systems incorporate the balanced combination of clinical and technological aspects to give an innovative healthcare outcome. Vital signs monitoring systems are rapidly becoming the core of today’s healthcare deliveries. The paradigm shifted from traditional and manual recording to computer based electronic records and further to smartphones as versatile and innovative healthcare monitoring systems. In this paper, the vital signs monitoring systems are classified as wearable, wireless and mobile monitoring systems and patient acceptability of some of these systems has been evaluated using 30 participants. Moreover, a comprehensive review of related literature in the context of acceptability, mobility, reliability and efficiency of vital signs monitoring systems in healthcare delivery and handling physiological measurements is presented. The outcome of this study indicates that despite some limitations commented by patients and clinicians, these systems should be more compact and simple to operate and they should be available to healthcare professionals with minimum interruption to normal daily life activities (ADLs).
|
|
| 19. |
- Moqeem, A., et al.
(författare)
-
Medical device integrated vital signs monitoring application with real-time clinical decision support
- 2018
-
Ingår i: Studies in Health Technology and Informatics, vol. 249. - : IOS Press. - 9781614998679 ; , s. 189-193
-
Konferensbidrag (refereegranskat)abstract
- This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.
|
|
| 20. |
- Rastegar, Solmaz, et al.
(författare)
-
Continuous Blood Pressure Estimation From Non-Invasive Measurements Using Support Vector Regression
- 2021
-
Ingår i: 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC). - : IEEE. - 9781728111797 ; , s. 1487-1490
-
Konferensbidrag (refereegranskat)abstract
- Blood pressure (BP) is one of the most crucial vital signs of the human body that can be assessed as a critical risk factor for severe health conditions such as cardiovascular diseases (CVD) and hypertension. An accurate, continuous, and cuff-less BP monitoring technique could help clinicians improve the prevention, detection, and diagnosis of hypertension and manage related treatment plans. Notably, the complex and dynamic nature of the cardiovascular system necessitates that any BP monitoring system could benefit from an intelligent technology that can extract and analyze compelling BP features. In this study, a support vector regression (SVR) model was developed to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) continuously. We selected a set of features commonly used in previous studies to train the proposed SVR model. A total of 120 patients with available ECG, PPG, DBP and SBP data were chosen from the Medical Information Mart for Intensive Care (MIMIC III) dataset to validate the proposed model. The results showed that the average root mean square error (RMSE) of 2.37 mmHg and 4.18 mmHg were achieved for SBP and DBP, respectively.
|
|
| 21. |
- Rastegar, S., et al.
(författare)
-
Estimating Systolic Blood Pressure Using Convolutional Neural Networks
- 2019
-
Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 143-149
-
Tidskriftsartikel (refereegranskat)abstract
- Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.
|
|
| 22. |
- Sabouri, Peyman, et al.
(författare)
-
A Cascade Classifier for Diagnosis of Melanoma in Clinical Images
- 2014
-
Ingår i: The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE EMBC14. - 9781424479290 ; , s. 6748-6751
-
Konferensbidrag (refereegranskat)abstract
- Computer aided diagnosis of medical images can help physicians in better detecting and early diagnosis of many symptoms and therefore reducing the mortality rate. Realization of an efficient mobile device for semi-automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this paper, interactive object recognition methodology is adopted for border segmentation of clinical skin lesion images. In addition, performance of five classifiers, KNN, Naïve Bayes, multi-layer perceptron, random forest and SVM are compared based on color and texture features for discriminating melanoma from benign nevus. The results show that a sensitivity of 82.6% and specificity of 83% can be achieved using a single SVM classifier. However, a better classification performance was achieved using a proposed cascade classifier with the sensitivity of 83.06% and specificity of 90.05% when performing ten-fold cross validation.
|
|
