ArticleComputational intelligence and neuroscience2021
Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Application of artificial intelligence medical imaging aided diagnosis system in the diagnosis of pulmonary nodules.BMC medical informatics and decision making · 2025Article
- Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images.Scientific reports · 2023Article
- Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images.Scientific reports · 2023Article
- 3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study.Cancers · 2022Article
- Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.Frontiers in oncology · 2022Article
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2 authors.
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Abstract
Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.
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