Rapid detection of cardiac pathologies by neural networks using ECG signals (1D) and sECG images (3D)

Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. On the other hand, surface electrocardiography (sECG) is a little-explored technique for this diagnosis. sECGs are three-dimensional images (two dimensions in space and one in time). In this way, the signals were taken in one-dimensional format and analyzed using neural networks. Following the transformation of the one-dimensional signals to three-dimensional signals, they were analyzed in the same sense. For this research, two models based on LSTM and ResNet34 neural networks were developed, which showed high accuracy, 98.71% and 93.64%, respectively. This study aims to propose the basis for developing Decision Support Software (DSS) based on machine learning models. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Skin lesion detection and classification using convolutional neural network for deep feature extraction and support vector machine

Pigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account