Classification of Breast Cancer Patients using Deep Learning Techniques
Tukaram Navghare, Aniket Avinash Muley, Vinayak Jadhav · Indian Journal of Science and Technology · 2024
Objectives: Breast cancer is one of the most ubiquitous cancers among women in the world and early exploration of the disease can be lifesaving. Finding breast cancer at an early stage enables quicker initiation of treatment, thereby enhancing the prospects of a positive outcome. Our aim is to identify the deep learning neural network model to classify breast cancer patients. Here, secondary open source data is considered to classify malignant and benign patients suitably. Methods: Deep learning neural network model, Artificial Neural Network and simulation approach is used to identify the more precise model. Findings: It is observed that, our proposed neural network model specified 97.5% accuracy. Efficiency of the proposed model is evaluated with the performance measures viz., MSE, RMSE etc. Novelty: The results of the study obtained through the proposed model express the efficiency of the model itself and also the superiority is demonstrated by comparing it with SVM, ANN, linear regression, 3DCNN deep models and existing works using various case studies. In the future, this model can be applicable in similar studies and it will give better results. Keywords: Deep learning, Artificial Neural Network, Breast cancer, Classification, Wisconsin data set