ArticleDiagnostics (Basel, Switzerland)2021
Analysis of Brain MRI Images Using Improved CornerNet Approach.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 50 citations in OpenAlex.
- A novel residual network based on multidimensional attention and pinwheel convolution for brain tumor classification.Scientific reports · 2025Article
- Lightweight-CancerNet: a deep learning approach for brain tumor detection.PeerJ. Computer science · 2025Article
- Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2024Article
- Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".Diagnostics (Basel, Switzerland) · 2022Article
- Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
- MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and FuzzyComputational and mathematical methods in medicine · 2022Article
- AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.Frontiers in medicine · 2022Article
- DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification.Frontiers in plant science · 2022Article
- Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.
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