Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

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