Multistage classification of oral histopathological images using improved residual network

<abstract> <p>Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.</p> </abstract>

LEGAL NATURE OF DEPARTMENTAL REGULATIONS

The article is devoted to the establishment of the legal nature of departmental regulations. The main parameters and scope of rule-making competence of federal executive bodies are examined. Because of the heterogeneity of departmental normative acts, there is a need to develop a general multistage classification of departmental regulations, the creation of which will also help determine their legal nature. Based on the analysis, a conclusion was made on the need for legislative regulation of the rule-making competence of federal executive bodies, as well as the procedure for implementing a departmental norm-setting process in the Russian Federation. The question is debated whether the departmental normative acts are sources of law. It is concluded that departmental regulations perform the same functions as all other normative legal acts, that is, they create norms of law, modify and supplement existing norms, and in some cases cancel them. Consequently, these acts are sources (forms) of law. The problem of the place, which departmental normative acts occupy in the system of subordinate normative acts, is being discussed. In conclusion, the author comes to the inference that departmental regulations possess all the features of by-laws. It is noted that departmental regulations act as acts of developing norm-setting, since they carry out the functions of detailing and concretizing laws, acts of the President and the Government.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.