2022 7th International Conference on Communication and Electronics Systems (ICCES) · 2022 · 11 citations · 0 references
ComputingComputer ScienceOncologyArtificial IntelligenceMachine LearningNeuro-oncologyBrain TumorsComputer-aided DiagnosisDeep LearningMedical Image AnalysisNeural NetworksTumor ClassificationArtificial Intelligence For DetectionBrain Tumor DiagnosisBrain Tumor PathologyComputational Biology ModelsComputer-aided Tumor DiagnosisDeep Learning-based Lesion ClassificationMachine Learning-based SegmentationMedical Image Classification
Simulation approaches based on Deep Learning (DL) techniques have several computational stages that present information at various levels of complexity. DL has exploded in popularity over the last decade, mainly in medical image analysis, information retrieval, and computational biology. As a result, DL has effectively transformed and enhanced detection, forecast, and treatment in various medical sectors, including pathological, brain tumors, lung disease, the stomach, heart, and eye. This study introduces a unique classification fuzzy logic-based and deep neuro (DNFLT) method compared with an SVM classifier for accurate segmentation of various brain tumors. According to the experimental observations, the suggested brain tumor classification approach is more resilient than other conventional methodologies of performance indicators, sensitivities, specificity, and correctness.