Article: Analogy of cervical malignancy through Inception V3 and Xception network of CNN Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2024 Vol.16 No.6 pp.523 - 536 Abstract: Cancer is a disease formed by the abnormal growth of cells and if it is not treated in its early stage, it spreads to other parts of the body. There are more than a hundred types of cancer available in the world. An effective method of testing is needed to diagnose the presence or absence of disease, monitor progress of the cancer, and evaluate the treatment's effectiveness. Cervical cancer ranks fourth among the cancers afflicting women worldwide. To overcome the aforementioned problem, deep learning techniques are used for automatically diagnosing the disease from Pap smear images. The proposed model is evaluated on SIPAKMED dataset. CNN-based architectures such as Inception V3 and Xception were used to classify the cervical cells and their accuracy was ascertained. The performance measures such as precision, recall and sensitivity are calculated. The obtained results concluded that CNN pre-trained model Xception achieved the higher classification rate of 95.99%. Inderscience Publishers - linking academia, business and industry through research

Title: Analogy of cervical malignancy through Inception V3 and Xception network of CNN

Authors: K. Hemalatha; N. Kasthuri; N.S. Kavitha; T. Jamuna; K. Kanchana

Addresses: Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India

Abstract: Cancer is a disease formed by the abnormal growth of cells and if it is not treated in its early stage, it spreads to other parts of the body. There are more than a hundred types of cancer available in the world. An effective method of testing is needed to diagnose the presence or absence of disease, monitor progress of the cancer, and evaluate the treatment's effectiveness. Cervical cancer ranks fourth among the cancers afflicting women worldwide. To overcome the aforementioned problem, deep learning techniques are used for automatically diagnosing the disease from Pap smear images. The proposed model is evaluated on SIPAKMED dataset. CNN-based architectures such as Inception V3 and Xception were used to classify the cervical cells and their accuracy was ascertained. The performance measures such as precision, recall and sensitivity are calculated. The obtained results concluded that CNN pre-trained model Xception achieved the higher classification rate of 95.99%.

Keywords: cervical cancer; SIPAKMED; Xception; Inception V3; deep learning.

DOI: 10.1504/IJMEI.2024.141792

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.6, pp.523 - 536

Received: 16 Feb 2022
Accepted: 11 Apr 2022
Published online: 02 Oct 2024 *

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Title: Analogy of cervical malignancy through Inception V3 and Xception network of CNN

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