## 2 Literature Review  
[PERSON] et al. [4] extracted features that describe color, texture, and morphology from 367 and 268 H&E image patches, which were acquired from tissue microarray (TMA) datasets. These features were used for support vector machine (SVM) classification. They achieved an accuracy of 96.7% and 81% for predicting benign vs. malignant and low-grade vs. high-grade classifications, respectively, using 5-fold cross-validation. [PERSON] et al. [5] proposed a cascade approach to the multi-class grading problem. They used cascade binary classification to maximize inter- and intra-class accuracy rather than the conventional one-shot classification and one-versus-all approaches to multi-class classification. In the proposed cascade approach, each division is classified separately and independently. [PERSON] et al. [6] proposed some novel features based on intra- and inter-nuclei properties for classification. They trained their classifier on 333 tissue microarray (TMA) cores annotated by six pathologists for different Gleason grades and used SVM classification to achieve an accuracy of 88.5% and 73.8% for cancer detection (benign vs. malignant) and low vs. high grade (Grade 3 vs. Grade 4, 5), respectively. [PERSON] et al. [7] extracted nearly 600 image texture features to perform pixel-wise Bayesian classification at each image scale to obtain the corresponding likelihood scene. The authors achieved an accuracy of 88.0% for distinguishing between benign and malignant samples. [PERSON] et al. [8] proposed Fuzzy C-Means (FCM) clustering algorithm for prostate multispectral MRI morphologic data processing and segmentation. The authors used co-registered T1w and T2 wMR image series and achieved an average dice similarity coefficient 90.77 \(\pm\) 7.75, with respect to 81.90 \(\pm\) 6.49 and 82.55 \(\pm\) 4.93 by processing T2w and T1w imaging alone, respectively. [PERSON] et al. [9] used combined deep learning and SVM methods for breast masses classification. The methods were applied to the Digital Database for Screening Mammography (DDSM) dataset and achieved high accuracy under two objective evaluation measures. The authors used nearly 600 images, out of these, 50% were benign and 50% were malignant. The classification accuracy achieved in this paper was 96.7% for distinguishing between benign and malignant samples. [PERSON] et al. [10] presented a novel mass detection system for digital mammograms, which integrated a visual saliency model with deep learning techniques. The authors used combined deep learning and SVM methods for image and feature classification, respectively. They achieved an average accuracy of 91.5% in mass detection between cancer and benign datasets. [PERSON] et al. [11] presented a method for automated histopathology images. They have demonstrated the utility of glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of prostate cancer, breast cancer, and distinguishing between cancerous and benign breast histology specimen. The authors used a SVM classifier for classification of prostate images containing 16 Gleason grade 3 images, 11 grade 4 images, and 17 benign epithelial images of biopsy tissue. They achieved an accuracy of 95.19% for grade 3 vs. grade 4, 86.35% for grade 3 vs. benign, and 92.90% for grade 4 vs. benign. [PERSON] et al. [12] introduced a novel approach to grade prostate malignancy using digitized histopathological specimens of the prostate tissue. They have extracted tissue structural features from the gland morphology and co-occurrence texture features from 82 regions of interest (ROI) with \(620\times 550\) pixels to classify a tissue pattern into three major categories: benign, grade 3 carcinoma, and grade 4 carcinoma. The authors proposed a hierarchical (binary) classification scheme and obtained 85.6% accuracy in classifying an input tissue pattern into one of the three classes. [PERSON] et al.