Efficient, Ultra-facile Breast Cancer Histopathological Images Classification Approach Utilizing Deep Learning Optimizers

Abstract

Conventional approaches to breast cancer diagnosis are associated with drawbacks that ultimately affect the quality of diagnosis and subsequent treatment, pushing for the need for automatic and precise classification of breast cancer tumors. The advent of deep learning methods has witnessed an increasing interest in their applications in many tasks. The specific case of using convolutional neural networks with transfer learning has witnessed tremendous successes in many classification tasks. Nonetheless, with transfer learning, the sheer number of parameters associated with deep networks coupled with the distance disparity between source data and target data leave networks prone to overfitting, particularly in the case of limited data. Also, negative transfer may occur in the situation where the source and target domains are not related. This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images. Four deep learning optimization algorithms are leveraged and explored to ascertain how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy. By adopting a polynomial learning rate decay scheduling and implementing several data augmentation techniques that regulate overfitting and improve the generalization ability of the proposed model, accuracy, sensitivity, specificity, and Area Under the Curve values of 89.92%, 94.02%, 86.42%, and 0.884 (88.4%), respectively are reported.

Deep Learning in Skin Disease Image Recognition: A Review

“…To evaluate the performance of deep learning in skin disease image recognition, several performance indicators are used: accuracy (ACC) represents the percentage of correct prediction results in the total sample [100]; mean average precision (mAP) represents the average accuracy of all categories [101]; true positive rate (TPR), also known as sensitivity and Recall (R) [102], represents the probability of being predicted to be positive in the actual positive samples [103]; false positive rate (FPR) refers to the percentage of actual disease-free but judged to be disease-free; true negative rate (TNR) [104], also known as specificity, indicates that the actual disease-free is correctly judged to be disease-free; area under the receiver operating characteristic (ROC) curve (AUC) refers to the probability that the classifier outputs positive and negative samples, and the likelihood that the classifier outputs a positive sample is greater than that of the negative sample; ROC is the working characteristic curve of subjects, which shows the performance of classification model under all classification thresholds [105]. The specific performance indicators are shown in Table VIII.…”

A Machine Learning Approach for the Estimation of Severity of Psoriasis Disorder Using Depth-Wise Convolution Neural Network

“…The authors in (8) studied transfer learning and proposed their neural network for the analysis of breast cancer diagnosis which eliminated the distance disparity between source data and target data which is known to have overfitting results, particularly in the case of limited data in the area of skin disorders.…”