Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering

Abstract

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study

“…In recent years, successful applications of CNNs in various fields have been reported. In the field of medical imaging, CNN architectures have shown efficient results similar to or better than those of human experts [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. In this study, we proved that a CNN architecture can accurately detect mandibular third molars and predict paresthesia before third molar extraction.…”

“…Deep learning has developed rapidly in recent years, making it possible to automatically extract information in the medical field, from diagnosis using medical imaging to analysis of activity and emotional patterns [ 1 , 2 , 3 ]. The deep convolutional neural network (CNN), a type of deep learning, has been widely applied to medical images due to its high performance in detection, classification, quantification, and segmentation [ 4 , 5 , 6 , 7 , 8 ].…”

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

“…Supervised learning models are analyzed in conjunction with outcome variables to establish a mathematical representation between the selected characteristics and the target variables, a widely utilized method in radiomic analysis. Support vector machine (SVM) is a commonly employed promising discriminative classification technique and is a typical practice to introduce multiple classification models for profiling to achieve better performance (24,(71)(72)(73). For instance, Kim et al (71) showed that SVM, logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long and short-term memory network performed well for prostate cancer identification on tissue images.…”

“…Support vector machine (SVM) is a commonly employed promising discriminative classification technique and is a typical practice to introduce multiple classification models for profiling to achieve better performance (24,(71)(72)(73). For instance, Kim et al (71) showed that SVM, logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long and short-term memory network performed well for prostate cancer identification on tissue images. Many other supervised classifiers exhibit favorable learning abilities such as the least absolute shrinkage and selection operator-LR (74), multivariate Cox proportional hazards regression models (59), decision trees (75), and random forest (51).…”

Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques

“…The aim of this paper was to differentiate the tumor types (meningioma, glioma, and pituitary) by performing binary and multiclass classification using AI techniques. In this study, we developed a long short-term memory (LSTM) [8] neural network model and used ML classifiers, namely support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), random forest (RF), and linear discriminant analysis (LDA), to perform multiclass and binary classification. In general, LSTM is used and well suited in classifying and making predictions based on convolutional neural network (CNN)-extracted features [7] or time-series data.…”