Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images

Abstract

Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

“…The most straightforward approach is to feed the cell image directly into a deep CNN model to extract the feature maps, then use the output layer and a classifier to obtain the predicted category. Shanthi et al [54] designed a CNN architecture composed of three convolutional layers, three max-pooling layers and one fully connected layer.…”

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

“…The CNNs were capable of efficiently identifying biopsy-worthy findings (AUC 0.947) 18 . Shanthi et al were able to correctly classify microscopic cervical cell smears as normal, mild, moderate, severe and carcinomatous with an accuracy of 94.1%, 92.1% and 85.1%, respectively, using various CNNs trained with augmented data sets (original colposcopy, contour-extracted and binary image data) 19 . In the view of Försch et al, one of the main challenges, generally, to increased integration of AI algorithms in the assessment of pathology and diagnosis of histomorphological specimens is that, at present, only a fraction of histopathological data is in fact available in digital form and thus accessible for automated evaluation 20 .…”

“…Bei der automatisierten Befundung mikroskopischer Zervixzellabstriche konnten in der Arbeit von Shanthi et al anhand verschiedener mit augmentierten Datensätze trainierter CNN (Originalkolposkopie, konturiert extrahierte und binäre Bilddaten) in 94,1%, 92,1% und 85,1% eine korrekte Klassifikation in normal bzw. milde, moderate, schwere oder karzinomatöse Zellveränderungen vorgenommen werden 19 . Eine der maßgeblichen Herausforderungen in der verstärkten Integration von KI-Algorithmen bei der pathologischen Beurteilung und Diagnostik von histomorphologischen Präparaten generell besteht nach Ansicht von Försch et al darin, dass derzeit nur ein Bruchteil der histopathologischen Daten tatsächlich in digitaler Form vorliegt und somit überhaupt einer automatisierten Auswertung zugänglich ist 20 .…”

Meticulous Review: Cutting-Edge Cervix Cancer Stratification Using Image Processing And Machine Learning

“…Based on the Statistics derived from the WHO Global Health Estimates-2019 [4], [5], [6], out of 656 300 000 total population of females in India, 4 191 000 females died due to cervical cancer [7]. Even though cervical cancer falls into a category of cancers that can be readily prevented through vaccination [2] and has an extended pre-malignant phase [8], [9], [10] which can be utilized for detection and treatment of the disease, lack of awareness is the key reason for this mortality rate in developing countries [11], [9], [12], [13], [14]. Cervical cancer is developed in the cervix area of the vagina the entrance of the uterus in women [3].…”