BREAST TUMOR DIAGNOSIS USING CLASS ACTIVATION MAP BASED CONVOLUTIONAL NEURAL NETWORK

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SP23-ECE499-Thesis-Wang, Zikui.pdf

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https://hdl.handle.net/2142/124797

Title: BREAST TUMOR DIAGNOSIS USING CLASS ACTIVATION MAP BASED CONVOLUTIONAL NEURAL NETWORK
Author(s): Wang, Zikui
Issue Date: 2023-05-01
Keyword(s): Breast cancer; Ultrasound image; Deep learning; Convolutional neural networks
Date of Ingest: 2024-10-04T12:02:54-05:00
Abstract: Breast cancer remains the second leading cause of cancer death among women. Many researchers have developed different deep learning methods, such as Convolutional Neural Networks (CNNs), to diagnose breast cancer. Class-activation-map (CAM) is a technique used to visualize the concentration of deep learning models. While most researchers use CAM to validate the effectiveness of their methods, our CAM-loss-based CNN shows significant improvement in malignancy classification by actively incorporating CAM in the loss function. We demonstrate that our method, compared with the baseline model (ResNet-18), not only effectively improves localization accuracy but also outperforms in terms of the AUC (area under the receiver operating curve), ACC (accuracy), sensitivity, and precision.
Type of Resource: text
Genre of Resource: dissertation/thesis
Language: eng

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