Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network

  • Stephen Kiptoo Student, Jomo Kenyatta University of Agriculture and Technology, P.O.Box 62,000-00200 Nairobi, Kenya
  • Lawrence Nderu Lecturer, Jomo Kenyatta University of Agriculture and Technology, P.O.Box 62,000-00200 Nairobi, Kenya
  • Leah Mutanu Lecturer, United States International University USIU- Africa, P. O. Box 14634-00800 Nairobi, Kenya
Keywords: Convolutional Neural Networks (CNN), CNN- Architecture, Cervical Colposcopy, Regional Based Convolutional Neural Network (R-CNN)

Abstract

The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms.

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Published
2021-02-20
Section
Articles