| 1. |
- Ahmed, Anisuddin, et al.
(författare)
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Factors influencing delivery-related complications and their consequences in hard-to-reach areas of Bangladesh
- 2024
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Ingår i: Sexual & Reproductive HealthCare. - : Elsevier. - 1877-5756 .- 1877-5764. ; 40
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Tidskriftsartikel (refereegranskat)abstract
- Background and objectives: Bangladesh's high maternal mortality ratio is exacerbated by delivery-related complications, particularly in hard-to-reach (HtR) areas with limited healthcare access. Despite this, few studies have explored delivery-related complications and factors contributing to these complications among the disadvantaged population. This study aimed to investigate the factors contributing to delivery-related complications and their consequences among the mothers residing in the HtR areas of Bangladesh. Methods: Data were collected using a cross-sectional study design from 13 HtR sub-districts of Bangladesh between September 2019 and October 2019. Data from 1,290 recently delivered mothers were analysed. Results: Around 32% (95% CI: 29.7-34.8) of the mothers reported at least one delivery-related complication. Prolonged labour pain (21%) was the highest reported complication during the delivery, followed by obstructive labour (20%), fever (14%), severe headache (14%). Mothers with higher education, a higher number of antenatal care (ANC) visits, complications during ANC, employed, and first-time mothers had higher odds of reporting delivery-related complications. More than one-half (51%) of these mothers had normal vaginal delivery. Nearly one-fifth (20%) of mothers who reported delivery-related complications were delivered by unskilled health workers at homes. On the other hand, about one-fifth (19%) of the mothers without any complications during delivery had a caesarean delivery. Nine out of ten of these caesarean deliveries were done at the private facilities. Conclusion: Delivery-related complications are significantly related to a woman's reproductive history and other background characteristics. Unnecessary caesarean delivery is prominent at private facilities.
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| 2. |
- Ahnaf, S.M. Azoad, et al.
(författare)
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Understanding CNN's Decision Making on OCT-based AMD Detection
- 2021
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Ingår i: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 14-16 Sept. 2021. - : IEEE. - 9781665423632 - 9781665423649 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Age-related Macular degeneration (AMD) is the third leading cause of incurable acute central vision loss. Optical coherence tomography (OCT) is a diagnostic process used for both AMD and diabetic macular edema (DME) detection. Spectral-domain OCT (SD-OCT), an improvement of traditional OCT, has revolutionized assessing AMD for its high acquiring rate, high efficiency, and resolution. To detect AMD from normal OCT scans many techniques have been adopted. Automatic detection of AMD has become popular recently. The use of a deep Convolutional Neural Network (CNN) has helped its cause vastly. Despite having achieved better performance, CNN models are often criticized for not giving any justification in decision-making. In this paper, we aim to visualize and critically analyze the decision of CNNs in context-based AMD detection. Multiple experiments were done using the DUKE OCT dataset, utilizing transfer learning in Resnet50 and Vgg16 model. After training the model for AMD detection, Gradient-weighted Class Activation Mapping (Grad-Cam) is used for feature visualization. With the feature mapped image, each layer mask was compared. We have found out that the Outer Nuclear layer to the Inner segment myeloid (ONL-ISM) has more predominance about 17.13% for normal and 6.64% for AMD in decision making.
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| 3. |
- Islam, Sarder Tazul, et al.
(författare)
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An Efficient Binary Descriptor to Describe Retinal Bifurcation Point for Image Registration
- 2019
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Ingår i: Pattern Recognition and Image Analysis. - Cham : Springer. - 9783030313319 - 9783030313326 ; , s. 543-552
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Konferensbidrag (refereegranskat)abstract
- Bifurcation points are typically considered as landmark points for retinal image registration. Robust detection, description and accurate matching of landmark points between images are crucial for successful registration of image pairs. This paper introduces a novel descriptor named Binary Descriptor for Retinal Bifurcation Point (BDRBP), so that bifurcation point can be described and matched more accurately. BDRBP uses four patterns that are reminiscent of Haar basis function. It relies on pixel intensity difference among groups of pixels within a patch centering on the bifurcation point to form a binary string. This binary string is the descriptor. Experiments are conducted on publicly available retinal image registration dataset named FIRE. The proposed descriptor has been compared with the state-of-the art Li Chen et al.’s method for bifurcation point description. Experiments show that bifurcation points can be described and matched with an accuracy of 86–90% with BDRBP, whereas, for Li Chen et al.’s method the accuracy is 43–78%.
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| 4. |
- Jamil, Md Shafayat, et al.
(författare)
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Advanced GradCAM++ : Improved Visual Explanations of CNN Decisions in Diabetic Retinopathy
- 2023
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Ingår i: Computer Vision and Image Analysis for Industry 4.0. - New York : Taylor & Francis Group. - 9781003256106 - 9781032164168 - 9781032187624 ; , s. 64-75
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Bokkapitel (refereegranskat)abstract
- Convolutional neural network (CNN)-based methods have achieved state-of-the-art performance in solving several complex computer vision problems including assessment of diabetic retinopathy (DR). Despite this, CNN-based methods are often criticized as “black box” methods for providing limited to no understanding about their internal functioning. In recent years there has been an increased interest to develop explainable deep learning models, and this paper is an effort in that direction in the context of DR. Based on one of the best performing method called Grad-CAM++, we propose Advanced Grad-CAM++ to provide further improvement in visual explanations of CNN model predictions (when compared to Grad-CAM++), in terms of better localization of DR pathology as well as explaining occurrences of multiple DR pathology types in a fundus image. By keeping all the layers and operations as is, the proposed method adds an additional non-learnable bilateral convolutional layer between the input image and the very first learnable convolutional layer of Grad-CAM++. Experiments were conducted on fundus images collected from publicly available sources namely EyePACS and DIARETDB1. Intersection over Union (IoU) score between the ground truth and heatmap produced by the methods were used to quantitatively compare the performance.The overall IoU score for Advanced Grad-CAM++ is 0.179, whereas for Grad-CAM++ it is score 0.161. Thus an 11.18% improvement in agreement with the ground truths by the proposed method is inferable.
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| 5. |
- Protik, Pranta, et al.
(författare)
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Automated Detection of Diabetic Foot Ulcer Using Convolutional Neural Network
- 2023
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Ingår i: The Fourth Industrial Revolution and Beyond. - Singapore : Springer Nature. - 9789811980312 - 9789811980343 - 9789811980329 ; , s. 565-576
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Bokkapitel (refereegranskat)abstract
- Diabetic foot ulcers (DFU) are one of the major health complications for people with diabetes. It may cause limb amputation or lead to life-threatening situations if not detected and treated properly at an early stage. A diabetic patient has a 15–25% chance of developing DFU at a later stage in his or her life if proper foot care is not taken. Because of these high-risk factors, patients with diabetes need to have regular checkups and medications which cause a huge financial burden on both the patients and their families. Hence, the necessity of a cost-effective, re-mote, and fitting DFU diagnosis technique is imminent. This paper presents a convolutional neural network (CNN)-based approach for the automated detection of diabetic foot ulcers from the pictures of a patient’s feet. ResNet50 is used as the backbone of the Faster R-CNN which performed better than the original Faster R-CNN that uses VGG16. A total of 2000 images from the Diabetic Foot Ulcer Grand Challenge 2020 (DFUC2020) dataset have been used for the experiment. The proposed method obtained precision, recall, F1-score, and mean average precision of 77.3%, 89.0%, 82.7%, and 71.3%, respectively, in DFU detection which is better than results obtained by the original Faster R-CNN.
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| 6. |
- Saha, Sajib, et al.
(författare)
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Retinal image registration using log-polar transform and robust description of bifurcation points
- 2021
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Ingår i: Biomedical Signal Processing and Control. - : Elsevier. - 1746-8094 .- 1746-8108. ; 66
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Tidskriftsartikel (refereegranskat)abstract
- Registration of retinal image is a crucial and fundamental step in several medical diagnoses. In this paper we propose an innovative method for retinal image registration. The method applies log-polar transform to approximate the difference in scale and orientation among images. A novel descriptor named Combined Local Haar of Bifurcation points (CLHB) is proposed for robust description and precise matching of retinal bifurcation and cross-over points. Experiments are performed on retinal image registration datasets collected from private and public sources and consisting of a total of 484 fundus photographs (i.e. 242 pairs). The proposed method has been compared with the state-of-the-art Generalized Dual-Bootstrap Iterative Closest Point (GDP ICP), Hernandez-Matas et al., Saha et al., and Chen et al.’s methods and has been found to outperform them with a clear margin. On the publicly available FIRE dataset, our proposed method is found 2% more accurate than the best performing Saha et al.’s method. On the private dataset the method is found to be about 3% more accurate than the best performing method.
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| 7. |
- Sayed, Md. Abu, et al.
(författare)
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A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus Photographs
- 2019
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Ingår i: Artificial Intelligence in Medicine. - Cham : Springer. - 9783030216412 - 9783030216429 ; , s. 347-351
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Konferensbidrag (refereegranskat)abstract
- Segmentation of retinal blood vessels is an important diagnostic procedure in ophthalmology. In this paper we propose an automated blood vessels segmentation method that combines both supervised and un-supervised approaches. A novel descriptor named Local Haar Pattern (LHP) is proposed to describe retinal pixel of interest. The performance of the proposed method has been evaluated on three publicly available DRIVE, STARE and CHASE_DB1 datasets. The proposed method achieves an overall segmentation accuracy of 96%, 96% and 95% respectively on DRIVE, STARE, and CHASE DB1 datasets, which are better than the state-of-the-art methods.
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| 8. |
- Sayed, Md. Abu, et al.
(författare)
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An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods
- 2020
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Ingår i: IET Image Processing. - : Institution of Engineering and Technology (IET). - 1751-9659 .- 1751-9667. ; 15:1, s. 180-190
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Tidskriftsartikel (refereegranskat)abstract
- Segmentation of retinal blood vessels is a very important diagnostic procedure in ophthalmology. Segmenting blood vessels in the presence of pathological lesions is a majorchallenge. In this paper, an innovative approach to segment the retinal blood vessel in thepresence of pathology is proposed. The method combines both supervised and unsupervised approaches in the retinal imaging context. Two innovative descriptors named localHaar pattern and modified speeded up robust features are also proposed. Experiments areconducted on three publicly available datasets named: DRIVE, STARE and CHASE DB1,and the proposed method has been compared against the state-of-the-art methods. Theproposed method is found about 1% more accurate than the best performing supervisedmethod and 2% more accurate than the state-of-the-art Nguyen et al.’s method.
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