Classifying brain tumors on magnetic resonance imaging by using convolutional neural networks
MA Gómez-Guzmán, L Jiménez-Beristaín… - Electronics, 2023 - mdpi.com
The study of neuroimaging is a very important tool in the diagnosis of central nervous system
tumors. This paper presents the evaluation of seven deep convolutional neural network …
tumors. This paper presents the evaluation of seven deep convolutional neural network …
A holistic approach to identify and classify COVID-19 from chest radiographs, ECG, and CT-scan images using shufflenet convolutional neural network
Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of
COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs …
COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs …
TumorDetNet: A unified deep learning model for brain tumor detection and classification
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment
process and helps to save the lives of a large number of people worldwide. Because they …
process and helps to save the lives of a large number of people worldwide. Because they …
An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model
Introduction Recently, plant disease detection and diagnosis procedures have become a
primary agricultural concern. Early detection of plant diseases enables farmers to take …
primary agricultural concern. Early detection of plant diseases enables farmers to take …
Clinical decision support framework for segmentation and classification of brain tumor MRIs using a U-Net and DCNN cascaded learning algorithm
Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification
Accurate classification of magnetic resonance imaging (MRI) images of brain tumors is
crucial for early diagnosis and effective treatment in clinical studies. In these studies, many …
crucial for early diagnosis and effective treatment in clinical studies. In these studies, many …
TumorGANet: A transfer learning and generative adversarial network-Based data augmentation model for brain tumor classification
Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant
challenges due to the complexities of segmentation and the variability in tumor …
challenges due to the complexities of segmentation and the variability in tumor …
Performance evaluation of machine learning algorithms for sarcopenia diagnosis in older adults
Background: Sarcopenia is a progressive and generalized skeletal muscle disorder. Early
diagnosis is necessary to reduce the adverse effects and consequences of sarcopenia …
diagnosis is necessary to reduce the adverse effects and consequences of sarcopenia …
Deeplungnet: An effective dl-based approach for lung disease classification using cris
Infectious disease-related illness has always posed a concern on a global scale. Each year,
pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity …
pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity …
Efficient brain tumor grade classification using ensemble deep learning models
Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The
analysis of the brain with MRI scans is fundamental to the diagnosis because it contains …
analysis of the brain with MRI scans is fundamental to the diagnosis because it contains …