2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 121-137, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Bhawna Utreja 1 * , Reecha Sharma 2 , Amit Wason 3
Doi: https://doi.org/10.54216/JISIoT.150209
Early-stage disease diagnosis is critical for effective treatment, and software-aided design can analyze disease architecture for timely detection. Many fail to identify disease severity before it becomes chronic, contributing to global mortality rates. Breast cancer, a prime reason of death among women, can be treated if detected early. Computer-aided diagnosis aids practitioners in accurately assessing disease criticality. This paper introduces an automated diagnosis system utilizing an enhanced Grasshopper Optimization technique and a Deep Neural Network (DNN) classifier. The Grasshopper Algorithm optimally selects features from segmented images, extracted through SIFT and BRISK hybrid techniques. The DNN classifies breast cancer using a partitioned dataset for training and testing. Performance metrics, including accuracy, precision, F-measure, and recall, demonstrate that the proposed system significantly outperforms existing methods, with an F-measure improvement of 5.1% and an accuracy increase of 11.19%.
Breast cancer , Feature Extraction , Classification , Grasshopper Optimization , Deep Neural Network , Artificial Neural Network
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