Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

Abstract

Background: In recent days, the field of nano-technology is becoming popular due to increased rate of accurate detection and effectiveness in clinical patients using Computer-Aided Diagnosis (CAD). The high rate of precision with accuracy and classification of brain tumor as benign or malignant can be achieved with nano-scale imaging technology. This helps to enhance the superiority of life for brain tumor diseased patients. Results: In this work, we propose the novel Semantic nano-segmentation for the detection of brain tumors even in nano scale range. Proposed Semantic Nano-segmentation based on Advanced - Convolutional Neural Networks will help the radiologists to find the brain cancer even at early stages with nodules at very smaller size. The proposed method of Advanced - Convolutional Neural Networks (A-CNN) uses ResNet-50. Here the nano-image is taken as input and tumor image is segmented using Semantic Nano-segmentation which carries an average dice and SSIM values to be 0.2133 and 0.9704 respectively. The accuracy of 93.2% and 92.7% is obtained by the proposed Semantic nano segmentation for benign and malignant tumor images respectively. A-CNN method of automatic classification has an average accuracy of 99.57% and 95.7% for benign and malignant images respectively. Conclusion: This novel nano-method is created for effective detection of tumor area in nanometers (nm) and thus evaluates the disease perfectly. Closeness of the Proposed method at ROC curve with reference to True Positive values indicates higher performance than other methods. Comparative analysis on ResNet-50 with testing and training data at rate of 90% -10%, 80%-20% and 70%-30% respectively is made which proves the effectiveness of the proposed work.