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Fulltext Enhancing diabetic retinopathy classification using deep learning

Alwakid G, Gouda W, Humayun M, Jhanjhi NZ

Digit Health, 2023;9:20552076231203676.
PMID: 37766903 DOI: 10.1177/20552076231203676

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the "Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection" dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.
Fulltext Diagnosing Melanomas in Dermoscopy Images Using Deep Learning

Alwakid G, Gouda W, Humayun M, Jhanjhi NZ

Diagnostics (Basel), 2023 May 22;13(10).
PMID: 37238299 DOI: 10.3390/diagnostics13101815

When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.
Fulltext Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Healthcare (Basel), 2023 Jun 15;11(12).
PMID: 37372880 DOI: 10.3390/healthcare11121762

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.
Fulltext YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Diagnostics (Basel), 2023 Jul 05;13(13).
PMID: 37443674 DOI: 10.3390/diagnostics13132280

Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.
Fulltext A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Diagnostics (Basel), 2023 Jul 21;13(14).
PMID: 37510187 DOI: 10.3390/diagnostics13142442

Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
Fulltext Deep learning-enhanced diabetic retinopathy image classification

Alwakid G, Gouda W, Humayun M, Jhanjhi NZ

Digit Health, 2023;9:20552076231194942.
PMID: 37588156 DOI: 10.1177/20552076231194942

OBJECTIVE: Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR.
METHODS: The suggested methodology presents two examples, one with and one without picture augmentation. A balanced dataset meeting the same criteria in both cases is then generated using augmentative methods. The DenseNet-121-rendered model on the Asia Pacific Tele-Ophthalmology Society (APTOS) and dataset for diabetic retinopathy (DDR) datasets performed exceptionally well when compared to other methods for identifying the five stages of DR.
RESULTS: Our propose model achieved the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100% for the APTOS dataset, and the highest test accuracy of 79.67%, top-2 accuracy of 92.%76, and top-3 accuracy of 98.94% for the DDR dataset. Additional criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS and DDR.
CONCLUSIONS: It was discovered that feeding a model with higher-quality photographs increased its efficiency and ability for learning, as opposed to both state-of-the-art technology and the other, non-enhanced model.
An Overview of the Recent Progress in Polymeric Carbon Nitride Based Photocatalysis

Humayun M, Ullah H, Tahir AA, Bin Mohd Yusoff AR, Mat Teridi MA, Nazeeruddin MK, et al.

Chem Rec, 2021 Jul;21(7):1811-1844.
PMID: 33887089 DOI: 10.1002/tcr.202100067

Recently, polymeric carbon nitride (g-C3 N4 ) as a proficient photo-catalyst has been effectively employed in photocatalysis for energy conversion, storage, and pollutants degradation due to its low cost, robustness, and environmentally friendly nature. The critical review summarized the recent development, fundamentals, nanostructures design, advantages, and challenges of g-C3 N4 (CN), as potential future photoactive material. The review also discusses the latest information on the improvement of CN-based heterojunctions including Type-II, Z-scheme, metal/CN Schottky junctions, noble metal@CN, graphene@CN, carbon nanotubes (CNTs)@CN, metal-organic frameworks (MOFs)/CN, layered double hydroxides (LDH)/CN heterojunctions and CN-based heterostructures for H2 production from H2 O, CO2 conversion and pollutants degradation in detail. The optical absorption, electronic behavior, charge separation and transfer, and bandgap alignment of CN-based heterojunctions are discussed elaborately. The correlations between CN-based heterostructures and photocatalytic activities are described excessively. Besides, the prospects of CN-based heterostructures for energy production, storage, and pollutants degradation are discussed.