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Although mammography is still the benchmark technique for breast cancer detection, many advantages of thermography make it a suitable adjunct tool for early detection. This paper describes the development of a computer-aided system for use together with thermography to assist in the detection and visualization/analysis of breast tumors. The system consists of a detection module for predicting the presence of tumors from thermograms, and a visualization module for generating the 3-D volumetric geometry of the suspected tumor inside the breast based on the 2-D thermogram. Detection is achieved through an artificial neural network taking the thermogram image as input, while the visualization is obtained by generating the 3-D model of the breast that produces a matching thermal image as the thermogram under a 3-D finite element analysis. A study with 200 subjects indicate that the detection sensitivity was good but the specificity was poor, but the reverse performance result was true for another back-propagation neural network which used physiological data instead of thermograms as input. This suggests that overall prediction capability can be improved by appropriate combination of the two results.

The treatment of early development of breast tumor has a higher success rate. This paper presents a framework for the early discovery of breast cancer. The objective is to assist the general practitioners and specialists in the detection of breast tumor. The proposed detection process consists of a preliminary screening process and a prediction process. The preliminary screening process using thermography aims to complement the detailed screening operation using mammography. The prediction process using artificial intelligence techniques aims to use past records of other similar cases to enhance the forecast of breast cancer development. The paper discusses the issues and techniques for the implementation of the proposed framework. These include the preliminary screening process, the retrieval of the relevant cases, and the prediction of the risk of developing breast cancer based on the thermographs, environmental/social data, physiological information, genetic factors, and medical records. This work constitutes initial effort to lessen the burden of medical professionals and increase the chances of successful treatment for patients in the fight against breast cancer.

Magnetic resonance imaging (MRI) plays an integral role among the advanced techniques for detecting a brain tumor. The early detection of brain tumor with proper automation algorithm results in assisting oncologists to make easy decisions for diagnostic purposes. This paper presents an automatic classification of MR brain images in normal and malignant conditions. The feature extraction is done with gray-level co-occurrence matrix, and we proposed a feature reduction technique based on statistical test which is preceded by principal component analysis (PCA). The main focus of the work is to establish the statistical significance of the features obtained after PCA, thereby selecting significant feature values for subsequent classification. For that, a $t$-test is performed which yielded a $p$-value of 0.05. Finally, a comparative study using $k$-nearest neighbor (kNN), support vector machine and artificial neural network (ANN)-based supervised classifiers is performed. In this work, we could achieve reasonably good sensitivity, specificity and accuracy for all the classifiers. The ANN classifier gives better performance with sensitivity of 97.33%, specificity of 97.42% and accuracy of 98.66% on the whole brain atlas database. The experimental results obtained are comparable to the other recent state-of-the-art.

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.

Heart sound (HS) analysis or auscultation is a standout amongst the most simple, non-invasive and costless methods used to evaluate heart health and is one of the basic and foremost routine of a doctor while reviewing a patient. Detecting cardiac abnormality by auscultation demands a physician’s experience and even then there is a high scope of committing error. In this paper, a low cost electronic stethoscope is built to acquire HS in a novel manner by taking one from each ventricular and auricular area and superimposed, to get a resultant signal of both distinct lub-dub sound. Then, a light, fast and low computation speed beat track method followed by wavelet reconstruction is presented for correct detection of S1 and S2. It is done without ECG reference, and can be used satisfactorily on both normal and pathological HSs. Moreover, heartbeats can be identified in both de-noised and noised environment as it is independent of external disturbances. Significant features are extracted from the resultant HSs with detected S1 and S2 and feed-forward back propagation method. It is used to classify the HS nature into normal and pathological. This algorithm has been implemented on 24 pairs of HSs, extracted from 24 patients of 15 pathological and nine normal subjects and the classification yields a result of 91.7% accuracy with 81.8% sensitivity. The overall performance suggests a good performance to cost ratio. This system can be used as first diagnosis tool by the medical professionals.

Repetitive transcranial magnetic stimulation (rTMS) is defined as a noninvasive technique of brain stimulation conducted for both diagnostic and therapeutic purposes. rTMS can effectively excite the brain neurons and increase brain plasticity, which becomes particularly useful in psychiatric and neurological fields. Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a noninvasive neurophysiological test that is promising as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel nonlinear index of the resting state EEG activity as a predictor of clinical outcome and compare its predictive capacity to traditional frequency-based indices. EEG was recorded from 50 patients with treatment resistant depression (TRD) and 24 healthy comparison (HC) subjects. TRD patients were treated with excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC) for 4–6 weeks. EEG signals were first decomposed using the ICA algorithm and the extracted components were then processed by time-frequency analysis. We then go on to compare the participants’ depression severity before, after, and 2 months after finishing the last treatment session using the proposed rTMS therapy. Absolute powers (APs), band powers (BPs), and theta and beta band entropies (BAs), which were extracted from the EEG, are used as features for the classification of changes in patients and normal cases after applying rTMS. Accordingly, we can go beyond the Beck score and clinically classify the EEG signal into two classes: depression and normal. The results demonstrated 78.37%, 74.32%, and 82.43% accuracy for artificial neural network (ANN), $k$-nearest neighbor (KNN), and support vector machine (SVM) classifiers, respectively, indicating the superiority of the proposed method to those mentioned in similar studies. Also, the electrophysiological changes are shown to be evident in patients with major depression. Our data show that the time-frequency index yields superior outcome prediction performance compared to the traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression.

The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis of cancer. Medical images are typically utilized by radiologists, engineers, and clinicians to spot the inner constitution of either individual patients or group of individuals. Most doctors prefer computed tomography (CT) images for initial screening of cancer — mainly lung cancer. To achieve deeper understanding and categorization of lung cancer, diverse machine learning techniques are employed in image classification. Many research works have been done on the classification of CT images with different algorithms, but they failed to reach 100% accuracy. By applying methods like Support Vector Machine, deep learning system like artificial neural network (ANN) and proposed convolution neural network (CNN), a computerized system can be built for truthful classification. The models are built as a classification system that can identify the nodule, if present in the lungs, as benign, malignant or normal or as benign or normal. Lung cancer datasets at Iraq National Center aimed at Cancer Diseases (IQ-OTHNCCD) and Iran Hospital-based CT images are used in this research. SVM, ANN, and proposed CNN classification techniques are applied to the datasets considered. This research work, proposes a model for classification of CT images with very promising accuracy on the datasets considered.