Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50

Stage-independent biomarkers for Alzheimer’s disease from the living retina: an animal study

The early diagnosis of neurodegenerative disorders is still an open issue despite the many efforts to address this problem. In particular, Alzheimer’s disease (AD) remains undiagnosed for over a decade before the first symptoms. Optical coherence tomography (OCT) is now common and widely available and has been used to image the retina of AD patients and healthy controls to search for biomarkers of neurodegeneration. However, early diagnosis tools would need to rely on images of patients in early AD stages, which are not available due to late diagnosis. To shed light on how to overcome this obstacle, we resort to 57 wild-type mice and 57 triple-transgenic mouse model of AD to train a network with mice aged 3, 4, and 8 months and classify mice at the ages of 1, 2, and 12 months. To this end, we computed fundus images from OCT data and trained a convolution neural network (CNN) to classify those into the wild-type or transgenic group. CNN performance accuracy ranged from 80 to 88% for mice out of the training group’s age, raising the possibility of diagnosing AD before the first symptoms through the non-invasive imaging of the retina.Tis study was supported by Te Portuguese Foundation for Science and Technology (FCT) through PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, UIDB/04539/2020, Pest-UID/NEU/04539/2019, and by FEDERCOMPETE through POCI-01-0145-FEDER-028039.info:eu-repo/semantics/publishedVersio

COVID-19 Detection on Chest x-ray Images by Combining Histogram-oriented Gradient and Convolutional Neural Network Features

The COVID-19 coronavirus epidemic has spread rapidly worldwide after a person became infected with a severe health problem. The World Health Organization has declared the coronavirus a global threat (WHO). Early detection of COVID 19, particularly in cases with no apparent symptoms, may reduce the patients mortality rate. COVID 19 detection using machine learning techniques will aid healthcare systems around the world in recovering patients more rapidly. This disease is diagnosed using x-ray images of the chest; therefore, this study proposed a machine vision method for detecting COVID-19 in x-ray images of the chest. The histogram-oriented gradient (HOG) and convolutional neural network (CNN) features extracted from x-ray images were fused and classified using support vector machine (SVM) and softmax. The proposed feature fusion technique (99.36 percent) outperformed individual feature extraction methods such as HOG (87.34 percent) and CNN (93.64 percent)

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

Desenvolvimento de uma rede neuronal de convolução para reconhecimento de hérnias discais em imagens de ressonância magnética

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Área de especialização: Imagem por Ressonância MagnéticaIntrodução: A hérnia discal lombar (HDL) é, atualmente, a causa mais frequente da radiculopatia lombar nos adultos jovens. A IA é considerada “a tecnologia que define o futuro”, pelo que é extremamente pertinente demonstrar a fiabilidade do uso desta tecnologia de elevado potencial no diagnóstico de HDL, fazendo uso da técnica imagiológica de maior sensibilidade e acurácia diagnóstica, a ressonância magnética. Objetivos: O objetivo principal deste trabalho é desenvolver e treinar uma rede neuronal de convolução (CNN) destinada a auxiliar o diagnóstico de HDL, tendo por base imagens de ressonância magnética da coluna lombar no plano axial. Métodos: O desenho de estudo é de carácter descritivo e estatístico, secundário, de recuperação e análise crítica da literatura. No total, foram recolhidas e analisadas 48 345 imagens totais de ressonância magnética da coluna lombar, referentes a 515 utentes, as quais se encontram disponíveis numa base de dados pública. Destas imagens, escolheram-se 3 172 ponderadas em T2 e referentes aos planos axial e sagital. Posteriormente, recorrendo a um algoritmo de data augmentation, foram geradas 35 600 imagens desenvolver destinadas a treinar e validar duas CNN (VGG16 e VGG19). Resultados: Foram alcançados excelentes valores de accuracy durante a validação das redes, com os melhores resultados a chegarem a cerca de 0,9; estes resultados foram acompanhados de funções de loss decrescentes no processo de validação que atingiram valores de 0,5. Conclusões: O contributo deste trabalho pode ser importante para o desenvolvimento de um algoritmo capaz de detetar HDL em imagens de ressonância magnética com uma precisão muito próxima da executada pelos profissionais de saúde mais experientes.ABSTRACT - Introduction: Lumbar disc herniation is currently the most frequent cause of lumbar radiculopathy in young adults. Artificial intelligence is considered “the technology that defines the future”, so it is extremely pertinent to demonstrate the reliability of the use of this high-potential tool in the diagnosis of lumbar disc herniations, through the imaging method of greater sensitivity and diagnostic accuracy, magnetic resonance imaging. Objectives: The main objective of this work is to demonstrate the applicability and reliability of convolutional neural networks in the diagnosis of lumbar disc hernias through the application of a convolutional neural network in magnetic resonance imaging. Methods: The study design is descriptive and statistical, secondary, recovery, and critical analysis of the literature. In total, 48 345 magnetic resonance images of the lumbar spine available in a public database were collected and analyzed, referring to 515 users. Of these images, 3 172 T2 – T2-weighted were chosen and referred to the axial and sagittal planes. Subsequently, using a data augmentation algorithm, 35,600 were selected to develop, train, and validate the CNN based on the VGG16 network. Results: Excellent accuracy values were achieved during network validation, reaching 0,9. The best loss function values in the validation process were 0,5. Conclusions: After the application of a convolutional neural network, it was found that this is a tool to be taken into account in the diagnosis of HDLs.N/