Hussain Ahmad
, Muhammad Zeb, Khalil-ur-Rahman
Cancer is among the deadliest diseases afflicting humanity. At present, there exists no successful therapy. Breast cancer is among the most common kinds of cancer. In 2020, the National Breast Cancer Foundation projected that approximately 276,000 fresh patients of invasive breast cancer and 48,000 fresh patients of non-invasive breast cancer were diagnosed in the USA. The patients have a 99% survival rate, as 64% of these cases are detected in initial stage of the disease. Artificial intelligence (AI) has been utilized to detect deadly diseases, which has enhanced the patient likelihood of survival by enabling early diagnosis and treatment. This research presented convolutional neural network (CNN) for the diagnosis of breast cancer disease automatically. The analysis has been carried out on a real-time invasive ductal carcinoma (IDC) dataset available at Kaggle. The dataset is preprocessed before being fed to CNN. The images is normalized to achieve a better accuracy. The developed model has an accuracy of 90% that is improved by 3% from the previous research paper. Different performances metrics are graphically represented in result section to analyze the model efficiency.
Hussain Ahmad Muhammad Zeb Khalil-ur-Rahman“An Intelligent Model for Detection of Breast Cancer based on Convolutional Neur Vol. 12 Issue 08 PP. 170-174 August 2025. https:// doi.org/10.5281/zenodo.16908308.
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