The role of convolution neural networks in detecting cancer

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M. Q. Hatem


Breast cancer has the second highest death rate among women, making it one of the most alarming cancers in terms of lethality. The number of breast cancer patients is predicted to substantially rise in the next years. Because early detection of cancer in general and breast cancer in particular, may save many lives, this stage must be completed precisely and without delay. As a result, developing an automated model to assist pathologists in correctly recognizing breast cancers and categorizing them as benign or malignant is critical. In this paper, we present a model that uses a Convolutional Neural Network (CNN) to effectively categorize breast cancers as benign or malignant based on histological findings. The suggested methodology is simple to use, provides quick results, and ensures precise breast cancer detection. The suggested model's loss value is 0.15066, while its accuracy value is 0.84934, according to the experimental data.

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How to Cite
Hatem, M. Q. (2024). The role of convolution neural networks in detecting cancer. Journal of Applied Research and Technology, 22(2), 180–188.