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

Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. This procedure is used to visualize pre-cancerous and abnormal areas in the cervix, thus a useful step in automating image regional-based cervical pre-cancerous lesion detection. This has revolutionized the early detection of cervical pre-cancerous traits. For cancerous ailments its detections in its earlier stages to a greater extent determines whether the ailment will be managed or not. This research uses R-CNN a digital colposcopy based convolutional neural network aimed at visualization of pre-cancerous lesions. It is mathematical modelbased classification algorithm that extracts the features used in determining the precancerous traits. This model was trained on a dataset comprising of 10,383 cervical images samples. The datasets were derived from Public Kaggle and UCI dataset repositories. The training samples comprised of grade 1, 2 and 3 traits of cervical precancerous traits. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous cases. Keywords— Convolutional Neural Networks (CNN), CNNArchitecture, Cervical Colposcopy, Regional Based Convolutional Neural Network (R-CNN)