Online ISSN: 2515-8260

Keywords : Image Segmentation


Analysis of Medical Images using Deep Neural Networks

Dr. Arata Kumar Swain, Dr. Suryasnata Sahoo, Dr. Harekrushna Dalei, Dr. Basanta Kumar Pradhan; Senthil Kumar S Zafar Ali Khan Jayachandran A

European Journal of Molecular & Clinical Medicine, 2022, Volume 9, Issue 3, Pages 10227-10238

Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consumes huge percentage of budget. Mostly the interpretations of medical data are being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real-world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health sector. we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue.

Plant Disease Identifer Using K-Means and GLSM in Convolution Neural Network

S.P. Vijaya Vardan Reddy; T. Suresh; K. Naresh Kumar Thapa; V. Ramkumar; S. Mahabhoob Basha; Deepika. Y

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1354-1360

Produces from agriculture which feeds the entire population is dependent on proper farming practices. The growth of technology must pay a way for increasing the produce per acre and also help in reducing the onset of frequently affecting plant disease. Timely help in detecting the diseases coupled with solution helps in productivity and quality of the produce. This paper aims to detect the plant leaf disease based on image detection and using machine learning to identify the disease with accuracy and suggest the solution. The product must cater to the needs of urban and rural farmer and also the person with only lay man knowledge of taking photo. This project mainly focuses on leaf disease like Anthracnose, Bacterial Blight, Cercospora, Alternaria Altermata diseases in the Pomegranate, Indian Beech, Tobacco, and Bitter Gourd leaves. This project aims to identify the disease even with lesser region of Interest and predict the leaf diseases using Convolutional Neural Network Algorithm