Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach
DOI:
https://doi.org/10.18201/ijisae.2022.272Keywords:
Deep Learning, BFscore, Dice Coefficient, Visual Geometry Group(VGG)Abstract
Sharpe and smooth pancreas segmentation is crucial and arduous problem in medical image analysis and investigation. A semantic deep learning bottom- up approach is most popular and efficient method used for pancreas seg- mentation with smooth and sharp result. Automatic pancreas segmentation process is performed through semantic segmentation for abdominal computed tomography(CT) clinical images. A novel semantic segmentation is applied for acute pancreas segmentation with different angle of CT images. In novel modified semantic approach 12 layers are used. The proposed model is exe- cuted on a dataset of 80 patient single phase CT image. For training purpose 699 images and testing purpose 150 images are taken from dataset with dif- ferent angle. Proposed approach is used for many organ segmentation from CT scans clinical images with high accuracy. Computation time period is reduced as related to the state-of-art. Validation accuracy is a 69.29% and loss values varies 1 to 0 only. Bfscore,Dice Coefficient, Jaccard Coefficient are used to calculate similarity index values between test image and expected output image only. The proposed approach achieved a dice similarity index score upto 81±7.43%. Class balancing process is executed with the help of class weight and data augmentation. In novel modified semantic segmen- tation, max-pooling layer, RELU layer, softmax layer, transposed conv2d layer and dicePixelClassification layer are used. DicePixelClassification is newly introduced and incorporated in novel method for improved results. VGG-16,VGG-19 and RSnet-18 deep learning models are used for pancreas segmentation.
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