Abstract
One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.
Highlights
Medical image classification and segmentation is a field, where deep learning can make a huge impact with promising results
The Faster R-convolutional neural network (CNN) extracts the bounding box of the tumor and it is followed by the Chan–Vese algorithm to obtain a precise tumor boundary for an accurate segmentation of the tumors. e Faster region-based convolutional neural network (R-CNN) model was able to generate the boundary boxes with 93.6% confidence interval and 99.81% accuracy
We have proposed R-CNN and Chan–Vese algorithms based model for meningioma and glioma brain tumor classification and segmentation. e proposed model is validated using Figshare dataset with 5fold cross-validation and objective quality metrics Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Peak Signal to Noise Ratio (PSNR), and Mean Absolute Error (MAE) are calculated to analyse the performance of the proposed architecture
Summary
Medical image classification and segmentation is a field, where deep learning can make a huge impact with promising results. It facilitates the automation of noninvasive imagingbased diagnosis. According to [6], the overall employment of radiologic and MRI technologists grows faster than the average for all occupations in the USA All these findings confirm that medical image-based diagnosis is favoured in the modern healthcare system. CNN is a class of layered deep neural network architecture built using convolution, activation, pooling, and fully connected layers to analyse visual imagery. In a fully connected CNN architecture, these operations are executed forward and backward, through forward learning and backpropagation as a designed architecture fine-tune, that is, training cycle, to optimize the decision-making capacity of the CNN architecture
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
