European Journal of Molecular & Clinical Medicine

A program has been planned and developed to diagnose and identify brain cancer. The program employs computer-based techniques in the detections of tumor fragments or tumors, and in photographs of various Astrocytoma brain tumor patients, it classifies the type of tumor utilizing Artificial Neural Network. For the diagnosis of the brain tumor, photographs of the patients afflicted by cancer were created utilizing image processing technique such as imaging segmentation,histogram equalization, image enhancement, morphologic surgery and feature extraction.Gray Level Co-occurrence Matrix (GLCM) is used for the detection of surface characteristics in the observed tumor. Such properties are contrasted with the functionality contained in the knowledge base.To order to identify various forms of brain cancers, a neuro fuzzy concept was eventually created. The entire system was verified in two stages: first, the phase of learning / training as well as second, the phase of recognition / testing.The device was equipped through documented MRI images from patients with impaired brain cancer from the Department of Radiology of Tata Memorial Hospital (TMH). Known brain cancer tests of impacted MRI scans are often obtained from TMH and used for device monitoring. The method has been shown to be effective in classifying these samples.

Agribusiness is the foundation of Indian economy. Plant health and food safety goes hand in hand. The health of green plants is of vital importance to everyone.Plant diseases being an impairment to the normal state of a plant, it interrupts or modifies plants vital functions. The proposed system helps in identification of plant disease and provides remedies that can be used as a defense mechanism against the disease. The database obtained from the Internet is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project .Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers are used for prediction.CNNs provide unparalleled performance in tasks related to the classification and detection of crop diseases. They are able to manage complex issues in difficult imaging conditions A prototype drone model is also designed which can be used for live coverage of large agricultural fields to which a high resolution camera is attached and will capture images of the plants which will act as input for the software, based of which the software will tell us whether the plant is healthy or not. With our code and training model we have achieved an accuracy level of 78%. Our software gives us the name of the plant species with its confidence level and also the remedy that can be taken as a cure.

India accounts for 26% of the words Tuberculosis population. The WHO’s Global TB Program states that in India, the number of people newly diagnosed with TB increased by 74% when compared to other countries from 1.2 million to 2.2 million between 2013 and 2019. Tuberculosis was and still remains a disease that causes high death rates in the country. Many of these deaths can be easily prevented if diagnosed at an early stage. The easiest, cost-effective and non-invasive method of detecting tuberculosis is through a frontal chest x-ray (CXR). But this requires a radiologist to manually examine and analyse each of the X-ray, considering the heavy patient count this puts a great burden on the resources available. A computer aided diagnosis system can easily mitigate this problem and can greatly help in reducing the cost. In recent times deep learning has made great progress in the field of image classification and has produced remarkable outputs in terms of image classification in various domains. But there still remains a scope for improvements when it comes to Tuberculosis detection. The aim of this study is toapply three pre-trained convolutional neural networks that have proven record in image classification on to publically available CXR dataset and classify CXR’s that manifest tuberculosis and compare their performances. The CNN models that are used on our CXR images dataset as a part of this study are VGG-16 ,VGG-19,AlexNet ,Xception and ResNet-50. Also visualization techniques have been applied to help understand the features whose weights played a role in the classification process. With the help of this system, we can easily classify CXR’s that have active TB and even CXR’s that show mild abnormalities, thus ensuring that high risk patients get the help they require on time.