An Intelligent Breast Cancer Classification and Prediction Model Using Deep Learning Approach

Among the health issues affecting women, breast cancer is a major concern. Compared to other malignancies, it has one of the higher fatality rates. Early detection of cancer patients enables medical professionals to provide a more accurate diagnosis and prognosis. The objective of the study is to evaluate the performance of the proposed CNN-based models in identifying the malignant or benign tumor types in patients based on their cancerous or non-cancerous status. The study aims to discover the important parameters that affect the model's performance during training, such as the number of convolutional layers, the quality of the training data, and the dependent variable. The study makes use of the Breast Cancer Histopathological data set that is readily available on Kaggle. It is frequently employed to evaluate CNN-based models in the healthcare sector. The paper reveals how deep learning approaches, particularly CNN models, can be used to provide robust feature representation and accurate patient predictions. The parameters for estimation performance were, in order, 97.39% precision, 97.42% accuracy, and 97.45% recall. The results of the study lend credence to the notion that applying deep learning techniques can assist doctors in making precise diagnoses, picking the most effective course of treatment, and keeping track of patients’ prognoses. It provides clinicians with a solution that is far better than standard practices. According to the study, using machine learning and deep learning techniques may greatly improve the management and interpretation of healthcare data.