scholarly journals Multistage classification of oral histopathological images using improved residual network

2021 ◽  
Vol 19 (2) ◽  
pp. 1909-1925
Author(s):  
Santisudha Panigrahi ◽  
◽  
Ruchi Bhuyan ◽  
Kundan Kumar ◽  
Janmenjoy Nayak ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Diffusion Problem ◽  
Response To Treatment ◽  
Occurrence Rate ◽  
Complex Nature ◽  
Candidate Model ◽  
Gradient Diffusion ◽  
Histopathological Images ◽  
Multistage Classification

<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>

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

scholarly journals Difficulties in the diagnosis of fungal rhinosinusitis – Literature review

2016 ◽  
Vol 6 (21) ◽  
pp. 11-17 ◽  
Author(s):  
Elena Patrascu ◽  
Claudiu Manea ◽  
Codrut Sarafoleanu
Keyword(s):  
Literature Review ◽  
Clinical Examination ◽  
Correct Diagnosis ◽  
Response To Treatment ◽  
Research Directions ◽  
Fungal Rhinosinusitis ◽  
Patients At Risk ◽  
Controversial Topic ◽  
Invasiveness Potential

Abstract Fungal rhinosinusitis is an important pathological entity, a highly controversial topic in the medical world today, by the various research directions it offers. In order to be able to predict a patient’s prognosis and his response to treatment, first we must have a classification of fungal rhinosinusitis. The authors considered it is important to make a distinction between invasive and noninvasive forms of fungal rhinosinusitis. The most important step in the management of fungal rhinosinusitis is to have a correct diagnosis, based on strong criteria, which will lead to a better prognosis of this disease. Because of its invasiveness potential, especially in patients at risk, it is essential to have a correct and fast diagnosis in case of fungal rhinosinusitis, in order to begin the treatment as fast as possible, for a favourable prognosis. The only way to establish diagnosis in a reliable way is to make a detailed clinical examination and to take biopsy samples.

scholarly journals Problems of improving conceptual apparatus in the field of protection and use of natural medicinal resources, medical and recreational areas and resorts

2019 ◽  
pp. 186-194
Author(s):  
N. G. Zhavoronkova ◽  
G. V. Vypkhanova
Keyword(s):  
Natural Resources ◽  
Legal Status ◽  
Legal Regulation ◽  
Complex Nature ◽  
Economic Relations ◽  
Conceptual Apparatus ◽  
Provision Of Services ◽  
Ecological Relations ◽  
Legal Concepts

The paper contains an analysis of theoretical problems associated with the conceptual apparatus in the sanatorium and resort sphere. They are largely due to the complex nature of the legal regulation of relations on the use and protection of natural medicinal resources, medical and recreational areas and resorts by the norms of legislation on public health, civil, urban planning, environmental, land and other branches of legislation. Accordingly, the assessment of legal concepts should cover the sphere of regulation of natural resource relations related to the use of natural resources for therapeutic and recreational purposes; the provision of services, the implementation of sanatorium-resort activities as an integral part of health and socio-economic relations; territorial (spatial) development of resort areas, medical and recreational areas; ecological relations due to the classification of such areas as specially protected. In the study of basic concepts — «health-improving terrain», «resort» — their characteristics such as «curative», «preventive», «wellness» are examined, contradictions in legislation are revealed, the necessity of expanding the criteria that are the basis for imparting with therapeutic natural resources, the corresponding legal status is justified. The necessity of expanding the terms and concepts related to the resort sphere — «resort infrastructure», «resort infrastructure user», «accommodation object», etc. is shown. The authors justify other proposals in the context of recent legislative initiatives in this area.

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