## 1 Introduction  
Image classification and analysis has become popular in recent years, especially for medical images. Cancer diagnosis and grading are often performed and evaluated using AI as these processes have become increasingly complex, because of growth in cancer incidence and the numbers of specific treatments. The analysis and classification of prostate cancer (PCA) are among the most challenging and difficult. PCa is the second most commonly diagnosed cancer among men in the USA and Europe, affecting approximately 25% of patients with cancer in the Western world [1]. PCa is a type of cancer that has always been an important challenge for pathologists and medical practitioners, with respect to detection, analysis, diagnosis, and treatment. Recently, researchers have analyzed PCa in young Korean men (<50 years of age), considering the pathological features of radical prostatectomy specimens and biochemical recurrence of PCa [2]. In the United States, thousands of people exhibit PCa. In 2017, there were approximately 161,360 new cases and 26,730 deaths, constituting 19% of all new cancer cases and 8% of all cancer deaths [3]. Therefore, it is important to detect PCa at an early stage to increase the survival rate. Currently, for the clinical diagnosis of PCa, methods that are performed in hospitals include a prostate-specific antigen test, digital rectal exam, trans- rectal ultrasound, and magnetic resonance imaging. Core needle biopsy examination is a common and useful technique, performed by insertion of a thin, hollow needle into the prostate gland to remove a tissue sample [4; 5; 6]. However, PCa diagnosis via microscopic biopsy images is challenging. Therefore, diagnostic accuracy may vary among pathologists. Generally, in histopathology sections, pathologists categorize stained microscopy biopsy images into benign and malignant. To carry out PCa grading, pathologists use the Gleason grading system, which was originally based on the sum of the two Gleason scores for the most common so-called Gleason patterns (GPs). Many studies conclude that this is the recommended methodology for grading PCa [7]. The Gleason grading system defines five histological patterns from GP 1 (well differentiated) to GP 5 (poorly differentiated), with a focus on the shapes of atypical glands [8; 9; 10; 11]. During the grossing study, the tumor affected in the prostate gland is extracted by the pathologist for examination under a microscope for cancerous cells [12; 13]. In this cell culturing process, the tissues are stained with hematoxylin and eosin (H&E) compounds, yielding a combination of dark blue and bright pink colors, respectively [14; 15; 16; 17; 18]. In digital pathology, there are some protocols that every pathologist follows for preparing and staining the tissue slides. However, the acquisition systems and staining process vary from one pathologist to another. The generated tissue images with the variations in colour intensity and artifacts could impact the classification accuracy of the analysis [19; 20]. DL and ML in AI have recently shown excellent performance in the classification of medical images. These techniques are used for computer vision tasks (e.g., segmentation, object detection, and image classification) and pattern recognition exploiting handcrafted features from a large-scale database, thus allowing new predictions from existing data [21; 22; 23; 24]. DL is a class of ML algorithms, where multiple layers are used to extract higher-level features gradually from the raw input. ML is a branch of AI concentrated on application building that learns from data. ML algorithms are trained to learn features and patterns in huge amounts of data to make predictions based on new data. Both DL and ML have shown promising results in the field of medical imaging and have the potential to assist pathologists and radiologists with an accurate diagnosis; this may save time and minimize the costs of diagnosis [25; 26; 27; 28].