Artificial Intelligence and Pattern Recognition · 2008 · 17 citations · 13 references
MedicineComputer ScienceOncologyComputer VisionDiagnostic ImagingMachine LearningCancer DetectionCancer Early DetectionComputer-aided DiagnosisEarly Cancer DetectionTexture AnalysisTumor DetectionBrain Tumor DiagnosisCluster ClassificationComputer-aided Tumor DiagnosisEarly Tumor DetectionFuzzy Logic ClassificationMedical Image ClassificationMinimum DistanceTexture Features
Computer aided diagnosis systems for detecting malignant texture in biological study have been investigated using several techniques. This paper presents an approach in computer-aided diagnosis for early prediction of brain cancer using Texture features and neuro classification logic. The Tumor mass detection and Cluster micro classification is used as the processing method for cancer prediction. Nine distinct invariant features with calculation of minimum distance for the prediction of cancer are used for the prediction of tumor in a given MRI image. A neuro fuzzy approach is used for the recognition of the extracted region. The implementation is observed on various types of MRI images with different types of cancer regions.
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