Abstract: Accurate diagnosis of lung and breast cancer is crucial for effective patient treatment and management. This study presents a novel framework that integrates hybrid clustering and Convolutional Neural Network (CNN) based classification for improved diagnosis of lung and breast cancer. The integration of hybrid clustering allows for the identification of intricate patterns within the lung and breast cancer datasets, while CNN ensures effective feature extraction and classification. The results verified the effectiveness of the proposed approach in accurately clustering and classifying lung and breast cancer instances. Classification results reveal a high level of accuracy for both lung and breast cancer datasets, with lung cancer achieving an accuracy score of 0.9847 and breast cancer reaching an accuracy score of 0.9986. Precision, recall, and F1 scores further validate the robustness of the approach. The proposed approach demonstrates promising potential for accurate cancer diagnosis and prognosis.

Keywords: Breast Cancer, Lung Cancer, Clustering, Classification, Data Mining

Downloads: | DOI: 10.17148/IARJSET.2025.12807