Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.info:eu-repo/semantics/publishedVersio

Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis

abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Комплекс на основі машинного навчання для визначення динаміки температурних градієнтів поверхні серця і коронарних судин

Тема магістерської дисертації: «Комплекс на основі машинного навчання для визначення динаміки температурних градієнтів поверхні серця і коронарних судин». Обсяг роботи становить 104 сторінок, міститься 33 ілюстрацій, 11 формул, 15 таблиць. Загалом опрацьовано 52 джерела. Мета: Система на основі машинного навчання для автоматизацій аналізу термограм в динаміці, яка дає можливість оцінити успішність проведення операції на відкритому серці. Задачі: 1. Порівняти декілька методів класифікації термознімків та обрати оптимальний для виявлення градієнта температури, який вищий за 3 градуси. 2. Розробити спосіб класифікації термознімків серця для виявлення певних патологій, коли градієнт температури більше за 3 градуси Цельсія, використовуючи сучасні методи машинного навчання. 3. Розробити систему динамічного аналізу на основі нейронної мережі та визначити показники результативності проведення операції на відкритому серці. 4. Спроектувати та розробити інтерфейс цієї системи, який забезпечить зручне та інтуїтивно зрозуміле користування. 5. Проаналізувати методи покращення алгоритму класифікації нейронної мережі. Основні результати: було здійснено аналіз сучасних методів машинного навчання та на їх основі побудовано класифікатор для визначення градієнтів температури серця з точність 65% та на цій основі розроблено програмний додаток для динамічного аналізу термограм з можливістю надати кількісну характеристику успішності операції з ішемічними ушкодженнями серця.The topic of the Master thesis is: " A machine learning-based complex for determining temperature gradients dynamics of the surface of the heart and coronary arteries.". The size of the report is 104 pages, contains 33 illustrations, 11 formulas, 15 tables. In total, there have been processed 52 sources. Objective: Machine-based system for automation of thermogram analysis in dynamics, which makes it possible to assess the success of open heart surgery. Task: 1. Compare several methods of classification of thermal images and choose the optimal temperature gradient that is higher than 3 degrees. 2. To develop a method of classification of thermal images of the heart to detect certain pathologies when the temperature gradient is more than 3 degrees Celsius, using modern methods of machine learning. 3. Develop a system of dynamic analysis based on the neural network and determine the effectiveness of open heart surgery. 4. Design and develop the interface of this system, which will provide convenient and intuitive use. 5. Analyze methods for improving the neural network classification algorithm. Main results: analysis of modern machine learning methods was performed and on their basis a classifier for determining heart temperature gradients with an accuracy of 65% was built and on this basis a software application for dynamic analysis of thermograms was developed with the ability to quantify the success of ischemic heart surgery