Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning
Karuna Sharma1, Saurabh Mukherjee2
1Karuna Sharma, Assistant Professor, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
2Saurabh Mukherjee, Professor, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4835-4841 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6945018520/2020©BEIESP | DOI: 10.35940/ijrte.E6945.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.
Keywords: Deep learning, Convolutional Neural Network, Tranfer Learning, Data Augmentation, Breast tumor classification, Micro-Calcification.
Scope of the Article: Deep Learning.