A hybrid methodology for breast screening and cancer diagnosis using thermography.

Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally different from that of healthy neighboring tissues, making thermography an option to be considered in the fight against cancer because it does not use ionizing radiation, venous access, or any other invasive process, presenting no damage or risk to the patient. In this paper, we propose a hybrid computational method using the Dynamic Infrared Thermography (DIT) and Static Infrared Thermography (SIT) for abnormality screening and diagnosis of malignant tumor (cancer), applying supervised and unsupervised machine learning techniques. We use the area under receiver operating characteristic curve, sensitivity, specificity, and accuracy as performance measures to compare the hybrid methodology with previous work in the literature. The K-Star classifier achieved accuracy of 99% in the screening phase using DIT images. The Support Vector Machines (SVM) classifier applied on SIT images yielded accuracy of 95% in the diagnosis of cancer. The results confirm the potential of the proposed approaches for screening and diagnosis of breast cancer.

A dynamic decision model for diagnosis of dementia, Alzheimer's disease and Mild Cognitive Impairment.

CDSS (Clinical Decision Support System) is a domain within digital health that aims at supporting clinicians by suggesting the most probable diagnosis based on knowledge obtained from patient data. Usually, decision models used by current CDSS are static, i.e., they are not updated when new data are included, which could allow them to acquire new knowledge and enhance system accuracy. This paper proposes a dynamic decision model that automatically updates itself from classifier models using supervised machine learning algorithms. Our supervised learning process ranks several decision models using classifier performance measures, considering available patient data, filled by the health center, or local clinical guidelines. The decision model with the best performance is then selected to be used in our CDSS, which is designed for the diagnosis of D (Dementia), AD (Alzheimer's Disease), and MCI (Mild Cognitive Impairment). Patient datasets from CAD (Center for Alzheimer's Disease), at the Institute of Psychiatry of UFRJ (Federal University of Rio de Janeiro), and CRASI (Center of Reference in Attention to Health of the Elderly), at Antonio Pedro Hospital of UFF (Fluminense Federal University), are used. The main conclusion is that the proposed dynamic decision model, which offers the ability to be continuously refined with more recent diagnostic criteria or even personalized according to the local domain or clinical guidelines, provides an efficient alternative for diagnosis of Dementia, AD, and MCI.

Hybrid analysis for indicating patients with breast cancer using temperature time series.

Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an average accuracy of 95.38% was obtained.

Thermal Signal Analysis for Breast Cancer Risk Verification.

Breast cancer is the second most common cancer in the world. Currently, there are no effective methods to prevent this disease. However, early diagnosis increases chances of remission. Breast thermography is an option to be considered in screening strategies. This paper proposes a new dynamic breast thermography analysis technique in order to identify patients at risk for breast cancer. Thermal signals from patients of the Antonio Pedro University Hospital (HUAP), available at the Mastology Database for Research with Infrared Image - DMR-IR were used to validate the study. First, each patient's images are registered. Then, the breast region is divided into subregions of 3x3 pixels and the average temperature from each of these regions is observed in all images of the same patient. Features of the thermal signals of such subregions are calculated. Then, the k-means algorithm is applied over feature vectors building two clusters. Silhouette index, Davies-Bouldin index and Calinski-Harabasz index are applied to evaluate the clustering. The test results showed that the methodology presented in this paper is able to identify patients with breast cancer. Classification techniques have been applied on the index values and 90.90% hit rate has been achieved.