A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images.

Over the last few years, brain tumor-related clinical cases have increased substantially, particularly in adults, due to environmental and genetic factors. If they are unidentified in the early stages, there is a risk of severe medical complications, including death. So, early diagnosis of brain tumors plays a vital role in treatment planning and improving a patient's condition. There are different forms, properties, and treatments of brain tumors. Among them, manual identification and classification of brain tumors are complex, time-demanding, and sensitive to error. Based on these observations, we developed an automated methodology for detecting and classifying brain tumors using the magnetic resonance (MR) imaging modality. The proposed work includes three phases: pre-processing, classification, and segmentation. In the pre-processing, we started with the skull-stripping process through morphological and thresholding operations to eliminate non-brain matters such as skin, muscle, fat, and eyeballs. Then we employed image data augmentation to improve the model accuracy by minimizing the overfitting. Later in the classification phase, we developed a novel lightweight convolutional neural network (lightweight CNN) model to extract features from skull-free augmented brain MR images and then classify them as normal and abnormal. Finally, we obtained infected tumor regions from the brain MR images in the segmentation phase using a fast-linking modified spiking cortical model (FL-MSCM). Based on this sequence of operations, our framework achieved 99.58% classification accuracy and 95.7% of dice similarity coefficient (DSC). The experimental results illustrate the efficiency of the proposed framework and its appreciable performance compared to the existing techniques.

Multimodal Medical Image Fusion Based on Content-based and PCA-sigmoid.

OBJECTIVE: The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/ single-photon emission computed tomography) information. METHODS: We proposed a new medical image fusion method based on content-based decomposition, Principal Component Analysis (PCA), and sigmoid function. We considered Empirical Wavelet Transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer. RESULTS: In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion, and the results are measured using four significant quantitative metrics. CONCLUSION: Finally, we compared our proposed method with other state-of-the-art transformbased fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features.

MOTIVATION AND OBJECTIVE: Obstructive sleep apnea (OSA) is a sleep disorder identified in nearly 10% of middle-aged people, which deteriorates the normal functioning of human organs, notably that of the heart. Furthermore, untreated OSA is associated with increased hypertension, diabetes, stroke, and cardiovascular diseases, thereby increasing the mortality risk. Therefore, early identification of sleep apnea is of significant interest. METHOD: In this paper, an automated approach for OSA diagnosis using a single-lead electrocardiogram (ECG) has been reported. Three sets of features, namely moments of power spectrum density (PSD), waveform complexity measures, and higher-order moments, are extracted from the 1-min segmented ECG subbands obtained from discrete wavelet transform (DWT). Later, correlation-based feature selection with particle swarm optimization (PSO) search strategy is employed for getting an optimum feature vector. This process retained 18 significant features from initially computed 32 features. Finally, the acquired feature set is fed to different classifiers including, linear discriminant analysis, nearest neighbors, support vector machine, and random forest to perform per segment classification. RESULTS: Experiments on the publicly available physionet single-lead ECG dataset show that the proposed approach using the random forest classifier effectively discriminates normal and OSA ECG signals. Specifically, our method achieved an accuracy of 89% and 90%, with 50-50 hold-out validation and 10-fold cross-validation, respectively. Besides, in both these validation scenarios, our method obtained 96% of the area under ROC. Importantly, our proposed approach provided better performance results than most of the existing methodologies.

Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method.

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine.

Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to classify five types of heartbeats: normal, premature ventricular contraction, atrial premature contraction, left bundle branch block and right bundle branch block. In the first approach, ECG beats are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Later four parameters, namely the sample entropy, coefficient of variation, singular values, and band power of IMFs are extracted as features. In the second approach, the same features are computed from IMFs extracted using an empirical mode decomposition (EMD) algorithm. The features obtained from the two approaches are independently fed to the sequential minimal optimization-support vector machine (SMO-SVM) for classification. We used two arrhythmia databases for our evaluation: MIT-BIH and INCART. We compare the proposed approaches with existing methods using the performance measures given by the average values of (i) specificity, (ii) sensitivity, and (iii) accuracy. The first approach demonstrates significant performance with 98.01% sensitivity, 99.49% specificity, and 99.20% accuracy for the MIT-BIH database and 95.15% sensitivity, 98.37% specificity, and 97.57% accuracy for the INCART database.