Online ISSN: 2515-8260

Keywords : Convolution Neural Network (CNN)


Angelin Beulah. S; Kartikay Kaul; Daksh Chauhan; Hepsiba Mabel. V

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 215-231

Deep Neural Networks have demonstrated amazingly positive execution in the field of computer vision issues - object acknowledgment, discovery, and division. These techniques have been used in the clinical picture examination area. Convolutional neural systems (CNNs), a remarkable part of profound learning applications to visual purposes, have earned significant consideration in the most recent years because of its advanced exhibitions in computer vision applications. They have accomplished tremendous growth in the areas of object acknowledgment, recognition and division challenges. Our attention is on models being utilized, information pre-handling and readiness and fittingly preparing the subsequent information or picture. The U – Nets are a very powerful CNNs which has accuracy near to humans. We have created and exploited this CNN architecture, U-Net and have done image segmentation for the brain Magnetic Resonance Images (MRI). The
aim of our work is to fundamentally concentrate on the pre-processing of the MRI images, perform Skull Stripping using Deep CNN architecture U-Net and to perform image

Automated classification of Oral Squamous cell carcinoma stages detection using Deep Learning Techniques

Dr. Abinaya. R; Aditya. Y; Dr. Bala Brahmeswara Kadaru

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1111-1119

Deep learning have earned major popularity in the today world by captured best results in medical analysis field. This research explained the stages of Oral squamous cell carcinoma using the convolution neural network model in deep Learning. Whenever the pathologist examine the photomicrograph image they faced a lot of difficulties to process and finding the stages of oral squamous cell carcinoma into poorly differentiated, medium differentiated and low differentiated. To avoid the difficulties of stages differentiation, the convolution neural network model has been implemented in this research. In the methodology part of Deep learning basically needs large number of data to perform good result so in this work image augmentation was performed to improve the better performance level of deep learning. Finally segmentation has been implemented and the segmented values are given to the convolution neural networks and it gives better accuracy of 85% when compared with all other deep learning techniques