## 2 Related Work  
A CNN was first used on medical images by [PERSON] et al. [35; 36]. Their model (LeNet) succeeded in a real-world application and could recognize hand-written digits [37]. Subsequent CNN-based methods showed the potential for automated image classification and prediction, especially after the introduction of AlexNet, a system that won the ImageNet challenge. In this era, the categorizing and auto-detection of cancer in the histological sections using machine assistance have shown excellent performance in the field of early detection of cancer. [PERSON] et al. [38] developed a new CNN-based architecture for histopathological images, using the 3D multiparametric MRI data provided by PROSTATEx challenge. Data augmentation was performed through 3D rotation and slicing, to incorporate the 3D information of the lesion. They achieved the second-highest AUC (0.84) in the PROSTATEx challenge, which shows the great potential of deep learning for cancer imaging. [PERSON] et al. [39] used breast cancer samples from the BreaKHis dataset to perform multi-classification using subordinate classes of breast cancer (ductal carcinoma, fibroadenoma, lobular carcinoma, adenosis, Phylodes tumor, tubular adenoma, mucinous carcinoma, and papillary carcinoma). The author developed a new deep learning model and has achieved remarkable performance with an average accuracy of 93.2% on a large-scale dataset. [PERSON] et al. [12] performed k-means segmentation to separate the background cells from the microscopy biopsy images. They extracted morphological and textural features from for automated detection and classification of cancer. They used different types of machine learning classifiers (random forest, Support vector machine, fuzzy k-nearest neighbor, and k-nearest neighbor) to classify connectivity, epithelial, muscular, and nervous tissues. Finally, the author obtained an average accuracy of 92.19% based on their proposed approach using a k-nearest neighbor classifier. [PERSON] et al. [40] used multiparametric magnetic resonance images and presented a novel method for the grading of prostate cancer. They used VGG-16 CNN and an ordinal class classifier with J48 as the base classifier. The author used the PROSTATAx-2 2017 grand challenge dataset for their research work. Their method achieved a positive predictive value of 90.8%. [PERSON] et al. [3] proposed an automated CNN-based pipeline for prostate cancer detection using diffusion-weighted magnetic resonance imaging (DWI) for each patient. They used a total of 427 patients as the dataset, out of these, 175 with PCa and 252 patients without PCa. The author used five CNNs based on the ResNet architecture and extracted first order statical features for classification. The analysis was carried out based on a slice- and patient-level. Finally, their proposed pipeline achieved the best result (AUC of 87%) using CNN1. [PERSON] [41] performed machine learning classification for cancer detection and used a data sample of colon, liver, thyroid cancer. They applied different ML algorithms, such as deep boost, AdaBoost,XgBoost, and support vector machines. The performance of the algorithms was evaluated using the area under the curve (AUC) and accuracy on real clinical data used classification. [PERSON] et al. [42] proposed different methods for the analysis of breast cancer histopathology images. They discussed different techniques for tissue image analysis and processing like tissue components segmentation, nuclei detection, tubules segmentation, mitotic detection, and computer-aided diagnosis. Before discussing the different image analysis algorithms, the author gave an overview of the tissue preparation, slide staining processes, and digitization of histological slides. In this paper, their approach is to perform clustering or supervised classification to acquire binary or probability maps for the different stains. [PERSON] et al. [43] performed prostate cancer detection based on different image analysis techniques.