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Author: search.filters.author.Sharma, AayushmaHas files: No

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    Comparing AI Decision-Making with Expert Biomarkers: A Case Study on Diabetic Retinopathy Classification
    (2025-01-01)
    Sharma, Aayushma
    ;
    Paing, May Phu
    Artificial intelligence (AI) has become prevalent in the healthcare sector due to its ability to interpret complex medical images that may not be apparent to humans. Traditional black-box models were used to classify the disease, providing no information as to why certain things were labeled as such and not others. This paper utilizes the use of eXplainable-AI (XAI), specifically, Layer-wise Relevance Propagation (LRP) which generates mapping between AI decision and biomarker used by the ophthalmologist whereby enhancing results interpretability and transparency in the disease diagnostic tasks. VGG-16 incorporated with batch normalization and label smoothing was used for the classification tasks whereas LRP was employed to perform the heat-map generation to see if the feature extracted and used by AI was consistent with the experts' biomarkers. Our proposed model obtained a classification accuracy of 77.33%, where 165 out of 266 images were aligned with the ophthalmologist's prediction. Furthermore, the significance of heatmap generation was supported by a one-sample Z-test which revealed that the alignment between AI predictions and expert biomarkers is significantly greater than random, with a 95% confidence interval.
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    Advancing Breast Cancer Identification: Exploring Deep Learning Models for Improved Detection
    (2025-01-01)
    Samrankit, Thitiphon
    ;
    Surakiat, Kanyanut
    ;
    Tamang, Sudarshan
    ;
    Sharma, Aayushma
    ;
    Paing, May Phu
    (1) Background: Breast cancer is the uncontrolled growth of abnormal cells within the breast tissue, and it can be either malignant or benign. Malignant tumors are cancerous and have the potential to invade and spread to other parts of the body while benign tumors are non-cancerous. Traditional breast cancer screening methods have limitations from artifacts and positioning errors. (2) Methods: This study employs various deep learning models—GELAN-C (Generalized Efficient Layer Aggregation Network – Compact version), YOLOv8 (You Only Look Once – version 8), RTMDet (Real-Time Models for Object Detection), DETR (DEtection TRansformer), YOLO-NAS (You Only Look Once – Neural Architecture Search), and Detectron2—to identify the most effective and adaptable model for breast cancer diagnosis. (3) Results: From our experiment, GELAN- C outperformed other models providing exceptional accuracy with the highest mean average precision (mAP) scores of both thresholds. Specifically, GELAN-C achieved a mAP@0.50 of 0.983, which reflects overall object detection accuracy. Additionally, it maintained a strong mAP@0.50-0.95 of 0.868 indicating its robustness and precision across varying levels of detection difficulty.
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