Transfer learning supported accurate assessment of multiclass cervix type images

Abstract

Cervical cancer predominately affects women compared to lung, breast and endometrial cancer. Premature stage identification and proper treatment of this cancer may lead to 100% survival rate. The cervix type is very prominent in the detailed diagnosis of cervical cancer. High expertise and experienced gynecologist are required for an accurate diagnosis of cervical cancer. To reduce their burden, a model is proposed, based on deep learning that automatically classifies the cervix types. This paper presents Modified Deep Convolutional Neural Networks namely Modified VGG16 (MVGG16), Modified VGG19 (MVGG19), Modified ResNet50 (MRN50), Modified InceptionV3 (MIV3), and Modified InceptionResNetV2 (MIRNV2) for the classification of cervix type images. These modified networks are implemented using a Multiclass Support Vector Machine classifier. The performance metrics are tabulated and compared with pre-trained models. The simulation results show that MIRNV2 achieves the best performance compared to other models with an overall Accuracy of 92.91% and a Kappa score of 0.88. MIRNV2 model also gives better classification accuracy of 96.62% for type 1, 93.58% for type 2, and 95.61% for type 3 cervix images. Hence, this facilitates the application of MIRNV2 as a diagnostic tool to assist the gynecologist in the classification of cervix type images.

A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

“…Based on these parameters, performance evaluation metrics namely accuracy, specificity, precision, F1 score, recall, misclassification rate, false omission rate, FN rate, FP rate, and false discovery rate are determined. The mathematical expression for these variables is given by 47,48 $$$$ \mathrm{Accuracy}\ \left(\mathrm{ACC}\right)=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{TN}+\mathrm{FN}} $$$$ $$$$ \mathrm{Precision}\ \left(\mathrm{PRE}\right)=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} $$$$ $$$$ \mathrm{Recall}/\mathrm{Sensitivity}\ \left(\mathrm{SEN}\right)=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} $$$$ $$$$ F1\ \mathrm{Score}\ (F1S)=\frac{2\mathrm{TP}}{2\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} $$$$ $$$$ \mathrm{Negative}\ \mathrm{Predictive}\ \mathrm{Value}\ \left(\mathrm{NPV}\right)=\frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FN}} $$$$ $$$$ \mathrm{Specificity}\ \left(\mathrm{SPE}\right)=\frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FP}} $$$$ …”

“…Based on these parameters, performance evaluation metrics namely accuracy, specificity, precision, F1 score, recall, misclassification rate, false omission rate, FN rate, FP rate, and false discovery rate are determined. The mathematical expression for these variables is given by 47,48 Accuracy ACC…”