Classification of Pap smear images for Cervical cancer using Convolutional Neural Networ
Manasa Ungrapalli N S1, Myna A N2
1Manasa Ungrapalli N S, Department of information Science and Engineering, M S Ramaiah Institute of Technology, Bengaluru, India.
2Myna A N* , Department of information Science and Engineering, M S Ramaiah Institute of Technology, Bengaluru, India.
Manuscript received on October 14, 2019. | Revised Manuscript received on 21 October, 2019. | Manuscript published on November 10, 2019. | PP: 2801-2807 | Volume-9 Issue-1, November 2019. | Retrieval Number: J12260881019/2019©BEIESP | DOI: 10.35940/ijitee.J1226.119119
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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: Classification of Pap smear images for cervical cancer consists of two types namely, normal and abnormal cancerous cells. The dataset involves 7 sets of classes of cancerous images which have 3 sets of normal cancerous images and 4 sets of abnormal cancerous images. The proposed work performs two stages of classification. The first stage of the work is classifying the data as normal or abnormal cancerous cells. In the second stage of the work, the class of the cancer as normal columnar, normal intermediate, normal superficial, light dysplasia, moderate dysplasia, severe dysplasia and carcinoma_ in_ situ are classified. The proposed work gives good results for classifying images for 3 sets of classes and 4 sets of classes for normal cells and is able to classify and detect normal and abnormal cell accurately.
Keywords: Deep Learning, Convolutional Neural Network, Cervical Cancer, Pap Smear Iimages
Scope of the Article: Deep Learning