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Research Paper | Computer Science and Information Technology | India | Volume 12 Issue 12, December 2023
Automated Breast Cancer Diagnosis using Optimization Algorithm with Deep Learning on Histopathological Images
R. Gurumoorthy | Dr. M. Kamarasan
Abstract: In the domain of medical diagnostics, the research for effectual and accurate breast cancer (BC) detection has stimulated the incorporation of cutting-edge technologies, mainly deep learning (DL). By leveraging complex patterns and features extracted by deep neural networks (DNNs), this technique aims to revolutionize the diagnostic setting, providing a highly accurate and early identification of BC. In the case of increasing patient outcomes, this research notes an important step towards the comprehension of advanced, technology-driven diagnostic tools in the realm of BC detection. This study designs a wide-ranging architecture for automated breast cancer diagnosis employing chimp optimization algorithm with deep learning (ABC-COADL) algorithm. EfficientNet, known for its higher performance in image classification functions has been utilized to remove important features from mammogram images. The LSTM-based classification methodgoalis to offer precise and reliable diagnoses. For optimizing the performance of the developed system, the Chimp optimization algorithm (COA)could beutilized for hyperparameter tuning. Stimulated by the intelligent behavior of chimpanzees in problem-solving, COA modifies the dynamic nature of DL algorithms, proficiently navigating the huge search space for the best hyper parameters. The developed architecture is estimated on a wide-ranging database, representing its efficiency in automating BC diagnosis. Comparison analyses with existing approaches highlight the supremacy of the combined method with respect of distinct measures. The robustness of the model is further analysed through validation and cross-validation under diverse databases.
Keywords: Breast Cancer;Chimp Optimization Algorithm; Magnetic Resonance Imaging; EfficientNet; Deep Learning
Edition: Volume 12 Issue 12, December 2023,
Pages: 1506 - 1512