IEEE 2021: PYTHON MACHINE LEARNING | IMAGE PROCESSING

IEEE 2021:  Convolution Neural Network model to detect and classify Tuberculosis (TB) manifestation in X-ray

ABSTRACT:  In developing or poor countries, it is not the easy job to discard the Tuberculosis (TB) outbreak by the persistent social inequalities in health. The less number of local health care professionals like doctors and the weak healthcare apparatus found in poor expedients settings. The modern computer enlargement strategies has corrected the recognition of TB testificanduming.  In this paper, It offer a paperback plan of action using Convolutional Neural Network (CNN) to handle with um-balanced; less-category X-ray portrayals (data sets), by using CNN plan of action, our plan of action boost the efficiency and correctness for stratifying multiple TB demonstration by a large margin It  traverse the effectiveness and efficiency of shamble with cross validation in instructing the network and discover the amazing effect in medical portrayal classification. This plan of actions and conclusions manifest a promising path for more accurate and quicker. Tuberculosis healthcare facilities recognition.

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IEEE 2022:  Remote Sensing Image Scene Classification Using Deep Learning

ABSTRACT Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. To be specific, we discuss the main challenges of remote sensing image scene classification using Convolutional Neural Network-based remote sensing image scene classification methods, In addition, we introduce the image preprocessing technique used for remote sensing image scene classification and summarize the performance.

IEEE 2022:  Deep Learning for the Detection of COVID-19 Using Deep Learning                                     ABSTRACT:  Covid-19 disease is the one off the disorders. Though the symptoms are benign initially, they become more severe over time. Although for most people COVID-19 causes only mild illness, it can make some people very ill. More rarely, the disease can be fatal. Older people, and those with pre- existing medical conditions (such as high blood pressure, heart problems or diabetes) appear to be more vulnerable. In this project we are going to use chest CT Scan images for classify the covid-19. We are using Deep Learning algorithm name called Convolutional neural network for classify diagnose the disease and we able to a achieve the best accuracy.

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