Improved the detection and classification of breast cancer using hyper parameter tuning

Abstract

The early diagnosis of any disease can be curable with a small amount of human effort. Most people will not be able to detect their illness before it becomes chronic. Breast cancer has been one of the diseases that can be treated until it spreads to all parts of the body when the disease is diagnosed at an earlier stage.Breast cancer is causing an increase in death rates all over the world. The medical practitioner can diagnose diseases incorrectly due to misinterpretation. In both developed and emerging countries, breast cancer is one of the leading causes of death for women. Breast cancer can be detected and classified early on in its progression, allowing patients to get adequate treatment. For detecting and classifying breast cancer in breast cytology videos, deep learning frameworks are suggested. The suggested architecture uses the Convolutional layer and the pooling layer to remove features. The data would be classified using the dense layer.The Convolutional Neural Network and DenseNet are used to extract image features that are then fed into a fully connected layer for the classification of malignant and benign cellsFor image classification of breast cancer, the models are trained, validated, and tested.The distinction is based on CNN and DenseNet's different batch sizes and learning rates. To see how well the proposed CNN and DenseNet programs work, they are compared. In terms of accuracy, the suggested system will outperform CNN and DenseNet in the diagnosis and classification of breast tumors from histological photographs. Hyper parameter tuning is used to measure the efficiency of CNN and DenseNet.