Abstract
Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory $(LSTM)$ and Residual Net $(ResNet-101)$ , independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix $(GLCM)$ using non-deep learning classifiers such as support vector machine $(SVM)$ Gaussian Kernel, k-nearest neighbor-Cosine $(KNN-Cosine)$ , kernel naive Bayes, decision tree $(DT)$ and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife ten-fold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value $(PPV)$ , negative predictive value $(NPV)$ , accuracy $(AC)$ , Mathews Correlation Coefficient ( $MCC$ ), and area under the curve $(AUC)$ . The most remarkable performance was obtained using non-deep learning methods with $GLCM$ features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method $ResNet-101$ , we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that $ResNet-101$ deep learning outperformed than non-deep learning methods and $LSTM$ . Thus, the deep learning method $ResNet-101$ could be used as a better predictor for the detection of prostate cancer.
Highlights
The prostate is a walnut-sized male reproductive gland that produces and secretes fluid of an alkaline nature
Deep and non-deep classifiers like support vector machine (SVM) radial function, Naive Bayes, RUSBoost tree, and decision tree were used to evaluate the results for different features
Using deep learning classifiers on magnetic resource images (MRI)-extracted long short-term memory (LSTM) features to detect prostate cancer, it is evident that we have achieved (100%) specificity and positive predictive value, where the accuracy (99.48%) was obtained using the same
Summary
The prostate is a walnut-sized male reproductive gland that produces and secretes fluid of an alkaline nature. The skeletal muscles inside extend from the diaphragm to the apex. Four lobs of the prostate are the peripheral zone (PZ ), the transition zone (TZ ), the central zone (CZ ), and the anterior fibromuscular trauma, among the first three consists of 70%, 5%, and 25% tissue respectively. The latter consists of no glandular tissue [3].
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