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Author: search.filters.author.Ariyametkul, AwikaHas files: No

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    Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations
    (2025-07-01)
    Ariyametkul, Awika
    ;
    Paing, May Phu
    Background: Breast cancer is the most frequently diagnosed and leading cause of cancer-related mortality among women worldwide. The danger of this disease is due to its asymptomatic nature in the early stages, thereby underscoring the importance of early detection. Mammography, a specialized X-ray imaging technique for breast examination, has been pivotal in facilitating early detection and reducing mortality rates. In recent years, artificial intelligence (AI) has gained substantial popularity across various fields, including medicine. Numerous studies have leveraged AI techniques, particularly convolutional neural networks (CNNs) and You Only Look Once (YOLO)-based models, for medical image detection and classification. However, the predictions of such AI models often lack transparency and explainability, resulting in low trustworthiness. This study aims to address this gap by investigating three state-of-the-art versions of the YOLO algorithm—YOLO version 9 (YOLOv9), YOLO version 10 (YOLOv10), and YOLO version 11 (YOLO11)—trained on breast cancer imaging datasets, specifically the INbreast and Mammographic Image Analysis Society (MIAS) databases. Additionally, to address the challenges posed by the lack of explainability and transparency, we integrate seven explainable artificial intelligence (XAI) methods: Grad-CAM, GradCAM++, Eigen-CAM, EigenGrad-CAM, XGrad-CAM, LayerCAM, and HiResCAM. Methods: This study utilized two publicly available breast cancer image databases: INbreast: toward a Full-field Digital Mammographic Database and the MIAS dataset. Preprocessing steps were applied to standardize all images in accordance with the input requirements of the YOLO architecture, as these datasets were used to train the three most recent versions of YOLO. The YOLO model demonstrating the highest performance—measured by mean average precision (mAP), precision, and recall—was selected for integration with seven different XAI methods. The performance of each XAI technique was evaluated both qualitatively through visual inspection and quantitatively using several metrics, including matching ground truth (mGT), Pearson correlation coefficient (PCC), precision, recall, and root mean square error (RMSE). These methodologies were employed to interpret and visualize the “black box” decision-making processes of the top-performing YOLO model. Results: Based on our experimental findings, YOLO11 outperformed YOLOv9 (mAP 0.868) and YOLOv10 (mAP 0.926), achieving the highest mAP of 0.935, with classification accuracies of 95% for benign and 80% for malignant cases. Among the evaluated XAI techniques, HiResCAM provided the most effective visual explanations, attaining the highest mGT score of 0.49, surpassing EigenGrad-CAM (0.45) and LayerCAM (0.42) in both visual and quantitative evaluations. Conclusions: The integration of YOLO11 with HiResCAM offers a robust solution that combines high detection accuracy with improved model interpretability. This approach not only enhances user trustworthiness by revealing decision-making patterns and limitations but also provide insights into the weaknesses of the model, enabling developers to refine and improve AI performance further.
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    Explainable AI (XAI) for Breast Cancer Diagnosis
    (2024-01-01)
    Ariyametkul, Awika
    ;
    Tamang, Sudarshan
    ;
    Paing, May Phu
    Breast cancer is the leading cause of mortality and incidence among women worldwide. Mammography, an essential imaging technique, plays a pivotal role in both screening and diagnostic processes by facilitating early detection, which helps improve survival rates. Despite its effectiveness, interpreting mammographic images presents considerable challenges, necessitating the expertise of highly trained radiologists. Artificial intelligence (AI) is a powerful tool for managing large amounts of data and is increasingly used across numerous sectors, including medical applications. This research focuses on applying Convolutional Neural Networks (CNNs) to classify breast cancer from mammograms. We explored six different CNN models including simple ConvNet, AlexNet, VGG-16, GoogLeNet, XceptionNet, and DenseNet201. Our results indicate that DenseNet201 is the most suitable model for this task, achieving 99% accuracy. However, a limitation of AI is the lack of transparency and explanation, often referred to as the 'black box' problem. This vulnerability can be addressed through explainable artificial intelligence (XAI), which elucidates the processes behind AI's decision-making. We employed three different XAI methodologies, including LIME, GradCAM, and GradCAM++, to visualize the model's decision-making process.
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    Deep Learning for Segmentation of Brain Tumors
    (2024-01-01)
    Tamang, Sudarshan
    ;
    Ariyametkul, Awika
    ;
    Paing, May Phu
    ;
    Bui, Toan H.
    (1) Background: Meningioma, Glioma, and Pituitary tumors are some of the three most common brain tumors that have great adverse effects on normal functionalities of the brain. These tumors are hard to detect and take a significant amount of time manually. To overcome this problem, deep learning techniques help in automating tumor detection quickly. This helps in early detection of brain tumors and patients can receive treatment before the tumor gets worse. (2) Methods: An automated segmentation model was created using UNet as a base model along variation of ResNet architecture. Magnetic resonance imaging (MRI) scans with T1-weighted contrast-enhanced images having 128 × 128 pixels in dimensions, are categorized into 3 classes- Meningioma (MEN), Glioma (GLI), and Pituitary tumor (PIT). At last, the predicted tumor by the proposed model was compared with the ground truth label of the corresponding tumor class. Dice score and IoU were used as performance metrics of the model. (3) Results: In this study, the best performing model was ResNeXt50_32x4dUNet among the 7 models used. The mean test dice score from this model was 0.835 whereas the mean validation dice score was 0. 784. Similarly, the mean IoU of the test images was 73.0% and showed an acceptable performance in the segmentation of brain tumor.