Prostate Cancer Detection Using Deep Learning and Traditional Techniques

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 <inline-formula> <tex-math notation="LaTeX">$(LSTM)$ </tex-math></inline-formula> and Residual Net <inline-formula> <tex-math notation="LaTeX">$(ResNet-101)$ </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">$(GLCM)$ </tex-math></inline-formula> using non-deep learning classifiers such as support vector machine <inline-formula> <tex-math notation="LaTeX">$(SVM)$ </tex-math></inline-formula> Gaussian Kernel, k-nearest neighbor-Cosine <inline-formula> <tex-math notation="LaTeX">$(KNN-Cosine)$ </tex-math></inline-formula>, kernel naive Bayes, decision tree <inline-formula> <tex-math notation="LaTeX">$(DT)$ </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">$(PPV)$ </tex-math></inline-formula>, negative predictive value <inline-formula> <tex-math notation="LaTeX">$(NPV)$ </tex-math></inline-formula>, accuracy <inline-formula> <tex-math notation="LaTeX">$(AC)$ </tex-math></inline-formula>, Mathews Correlation Coefficient (<inline-formula> <tex-math notation="LaTeX">$MCC$ </tex-math></inline-formula>), and area under the curve <inline-formula> <tex-math notation="LaTeX">$(AUC)$ </tex-math></inline-formula>. The most remarkable performance was obtained using non-deep learning methods with <inline-formula> <tex-math notation="LaTeX">$GLCM$ </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">$ResNet-101$ </tex-math></inline-formula>, we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that <inline-formula> <tex-math notation="LaTeX">$ResNet-101$ </tex-math></inline-formula> deep learning outperformed than non-deep learning methods and <inline-formula> <tex-math notation="LaTeX">$LSTM$ </tex-math></inline-formula>. Thus, the deep learning method <inline-formula> <tex-math notation="LaTeX">$ResNet-101$ </tex-math></inline-formula> could be used as a better predictor for the detection of prostate cancer.