EARLY STAGE DETECTION AND CLASSIFICATION OF BREAST CANCER

One of the major diseases that affect young to old aged women in re-cent times is breast cancer. It almost ranks as the first cause for death in women across the world. The survival rate of people suffering with it ranges some-where between 40% and 60% depending on the development terms of particular countries. Hence, it becomes quite important to be able to diagnose such a dis-ease at a stage as early as possible, so the patient could look out on the available options for treatment. Therefore, in this project, we propose such a breast can-cer detection system which predicts the nature of the cancer, either benign or malignant by processing the mammographic image of the patient. The model basically uses a range of digital image processing techniques and also algo-rithms of ML in the process to output the prediction. It is trained using the MIAS breast cancer dataset. The input image is first resized, gray-scaled, and a gaussian filter is applied on it to remove background noises. It is then segment-ed and fed to the neural network, which gives the output prediction as an integer value (each value corresponding to a predicted class). The project also has a second stage where the severity of the cancer is also detected by taking input of other detailed attributes of the mammogram.