Lidiya Wubshet Habtemariam1, Elbetel Taye Zewde1,2, Gizeaddis Lamesgin Simegn1,2. 1. Biomedical Imaging Unit, School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. 2. AI and Biomedical Imaging Research unit, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Abstract
Purpose: Cervical cancer is the 4th most common cancer among women, worldwide. Incidence and mortality rates are consistently increasing, especially in developing countries, due to the shortage of screening facilities, limited skilled professionals, and lack of awareness. Cervical cancer is screened using visual inspection after application of acetic acid (VIA), papanicolaou (Pap) test, human papillomavirus (HPV) test and histopathology test. Inter- and intra-observer variability may occur during the manual diagnosis procedure, resulting in misdiagnosis. The purpose of this study was to develop an integrated and robust system for automatic cervix type and cervical cancer classification using deep learning techniques. Methods: 4005 colposcopy images and 915 histopathology images were collected from different local health facilities and online public datasets. Different pre-trained models were trained and compared for cervix type classification. Prior to classification, the region of interest (ROI) was extracted from cervix images by training and validating a lightweight MobileNetv2-YOLOv3 model to detect the transformation region. The extracted cervix images were then fed to the EffecientNetb0 model for cervix type classification. For cervical cancer classification, an EffecientNetB0 pre-trained model was trained and validated using histogram matched histopathological images. Results: Mean average precision (mAP) of 99.88% for the region of interest (ROI) extraction, and test accuracies of 96.84% and 94.5% were achieved for the cervix type and cervical cancer classification, respectively. Conclusion: The experimental results demonstrate that the proposed system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited.
Purpose: Cervical cancer is the 4th most common cancer among women, worldwide. Incidence and mortality rates are consistently increasing, especially in developing countries, due to the shortage of screening facilities, limited skilled professionals, and lack of awareness. Cervical cancer is screened using visual inspection after application of acetic acid (VIA), papanicolaou (Pap) test, human papillomavirus (HPV) test and histopathology test. Inter- and intra-observer variability may occur during the manual diagnosis procedure, resulting in misdiagnosis. The purpose of this study was to develop an integrated and robust system for automatic cervix type and cervical cancer classification using deep learning techniques. Methods: 4005 colposcopy images and 915 histopathology images were collected from different local health facilities and online public datasets. Different pre-trained models were trained and compared for cervix type classification. Prior to classification, the region of interest (ROI) was extracted from cervix images by training and validating a lightweight MobileNetv2-YOLOv3 model to detect the transformation region. The extracted cervix images were then fed to the EffecientNetb0 model for cervix type classification. For cervical cancer classification, an EffecientNetB0 pre-trained model was trained and validated using histogram matched histopathological images. Results: Mean average precision (mAP) of 99.88% for the region of interest (ROI) extraction, and test accuracies of 96.84% and 94.5% were achieved for the cervix type and cervical cancer classification, respectively. Conclusion: The experimental results demonstrate that the proposed system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited.
Authors: William Small; Monica A Bacon; Amishi Bajaj; Linus T Chuang; Brandon J Fisher; Matthew M Harkenrider; Anuja Jhingran; Henry C Kitchener; Linda R Mileshkin; Akila N Viswanathan; David K Gaffney Journal: Cancer Date: 2017-05-02 Impact factor: 6.860
Authors: Peng Guo; Koyel Banerjee; R Joe Stanley; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shelliane R Frazier; Randy H Moss; William V Stoecker Journal: IEEE J Biomed Health Inform Date: 2015-10-26 Impact factor: 5.772