ArticleBiomedicines2023
Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 54 citations in OpenAlex.
- Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.Scientific reports · 2025Article
- Explainable CNN for brain tumor detection and classification through XAI based key features identification.Brain informatics · 2025Article
- Automated Brain Tumor segmentation using hybrid YOLO and SAM.Current medical imaging · 2025Article
- Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.Scientific reports · 2024Article
- Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.Scientific reports · 2024Article
- Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.BMC medical imaging · 2024Article
- ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.International journal of biomedical imaging · 2024Article
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.Cancers · 2023Article
- Compressive Sensing of Medical Images Based on HSV Color Space.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.