Breast Cancer is a leading killer of women globally. It is a serious health concern caused by calcifications or abnormal tissue growth in the breast. Doing a screening and identifying the nature of the tumor as benign or malignant is important to facilitate early intervention, which drastically decreases the mortality rate. Usually, it uses ultrasound images, since they are easily accessible to most people and have no drawbacks as such, unlike in the other most famous screening technique of mammograms where in some cases you may not get a clear scan. In this thesis, the approach to this problem is to build a stacked model which makes predictions on the basis of the shape, pattern, and spread of the tumor. To achieve this, typical steps are pre-processing of images followed by segmentation of the image and classification. For pre-processing, the proposed approach in this thesis uses histogram equalization that helps in improving the contrast of the image, making the tumor stand out from its surroundings, and making it easier for the segmentation step. Through segmentation, the approach uses UNet architecture with a ResNet backbone. The UNet architecture is made specifically for biomedical imaging. The aim of segmentation is to separate the tumor from the ultrasound image so that the classification model can make its predictions from this mask. The metric result of the F1-score for the segmentation model turned out to be 97.30%. For classification, the CNN base model is used for feature extraction from provided masks. These are then fed into a network and the predictions are done. The base CNN model used is ResNet50 and the neural network used for the output layer is a simple 8-layer network with ReLU activation in the hidden layers and softmax in the final decision-making layer. The ResNet weights are initialized from training on ImageNet. The ResNet50 returns 2048 features from each mask. These are then fed into the network for decision-making. The hidden layers of the neural network have 1024, 512, 256, 128, 64, 32, and 10 neurons respectively. The classification accuracy achieved for the proposed model was 98.61% with an F1 score of 98.41%. The detailed experimental results are presented along with comparative data
