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@article{169720,
        author = {Sreeji K B and Dr.R.Khanchana},
        title = {Analyzing of Mammography Image Breast Cancer Detection Using CNN and Feature Selection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2107-2117},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169720},
        abstract = {The prognosis of breast cancer is greatly influenced by the timely and precise identification of breast lesions, including the differentiation between suspicious, non-cancerous, and cancerous cancer. In this work, we present a brand-new feature extraction and reduction technique for the detection of breast cancer in pictures from mammography. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN),random forest (RF), and support vector machine (SVM).The dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity.},
        keywords = {breast cancer; convolutional neural network (CNN);feature selection; feature classification; mammography images},
        month = {November},
        }