Mammary Tumor Screening

Mammary Tumor Screening using deep learning provides an innovative approach for early breast cancer detection. In this work, a model trained on Convolutional Neural Networks (CNNs) on the Kaggle Multi Cancer dataset, consisting of 10,000 high-resolution histopathological images of benign and malignant tumors. To improve model performance and lessen overfitting, preprocessing methods like resizing, normalisation, and data augmentation are used. The CNN model .The CNN model is designed for binary classification, and itsF1-score, recall, accuracy, and precision are used to assess performance. This inquiry seeks to fashion a faultless core, employing an exhaustive dataset used by a cancer detection system. The high-resolution dataset comes from Kaggle, consisting of histopathological images of both benign and malignant growths, specifically malignant breast tumors. It furnishes multiple images to procure thorough diagnostic evaluations quickly. Experimental results show high effectiveness, implying a high level of helpfulness. Every instance of breast cancer being detected early improves patient outcomes