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.
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.
