International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
www.irjet.net
Deep Learning Approach for Unprecedented Lung Disease Prognosis Mulla Abdul Faheem1 Department of CSE Sri Venkateswara College of Engineering and Technology (Autonomous) Chittoor, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This research project focuses on the development
meticulously evaluated patient data to determine the presence or absence of lung diseases. The central focus of this binary classification project revolved around utilizing chest X-ray images as input and disease detection as output, with the overarching objective of enhancing the accuracy and efficiency of diagnosing and treating lung diseases. This groundbreaking research has shed light on the innovative application of machine learning techniques for predicting and managing lung illnesses, ultimately culminating in improved patient outcomes. By embracing the potential of advanced technologies, this study paves the way for a future where early detection and accurate diagnosis of lung diseases become commonplace, thus revolutionizing the field of healthcare.
and implementation of a binary classification model for predicting lung diseases using x-ray images. By leveraging the power of machine learning algorithms, specifically those found in the field of computer science known as machine learning, we aim to accurately assign class labels to data from the problem area. Throughout the project, we utilize popular Python libraries such as Tensor Flow, Keras, and NumPy to enhance the prediction accuracy. The results of this study provide valuable insights into the prediction of lung diseases based on x-ray images, offering potential advancements in diagnostic and prognostic methodologies Key Words: Convolutional neural network, AI Lung Diseases classification, Machine Learning,
2. OBJECTIVE
1.INTRODUCTION
The rapid pace of global change exerts strain on people's health, with detrimental shifts in climate, environment, and lifestyle significantly increasing disease vulnerability. This opportune moment allows us to contribute to the solution, empowered by computers and abundant public data. By assisting those unable to afford medical care, my approach aims to alleviate medical expenses while giving back to the community. Utilizing a deep learning model, the project focuses on detecting lung diseases in images. Various lung xray datasets, encompassing Normal, Tuberculosis, Covid-19, and Pneumonia, are merged to form a comprehensive dataset.
Lung disease prediction utilizing X-ray images involves the intricate task of detecting the presence or absence of lung ailments within provided images. This exceptional project has successfully implemented a cutting-edge machine learning model and a state-of-the-art Convolutional Neural Network (CNN) architecture, leveraging powerful Python libraries such as NumPy and TensorFlow. The astonishing test accuracy of 91% has truly surpassed expectations, showcasing the project's triumph in achieving its primary objectives. Machine learning, an indispensable facet of artificial intelligence, empowers computers to acquire knowledge from past instances and discern intricate patterns within vast and noisy datasets. In the realm of medicine, machine learning plays a pivotal role in disease detection, enabling early and accurate diagnoses, which have the potential to save lives and alleviate the burden on healthcare systems. Lung diseases, being a leading cause of mortality, demand precise diagnoses and predictions to improve patient care significantly. By synergistically amalgamating patient data with chest X-ray images and employing deep learning techniques such as CNN, this groundbreaking study has delved into respiratory issues encompassing diseases like Corona, Tuberculosis, Pneumonia, and Lung Cancer. The ultimate goal was to develop robust models for diagnosing lung disorders, assisting doctors in making informed decisions that are crucial for patient well-being. Harnessing the power of machine learning and deep learning algorithms, this study
© 2023, IRJET
|
Impact Factor value: 8.226
The idea of the project: Detecting lung diseases using a deep learning model focuses on predicting the presence or absence of lung disease in given images. Diverse lung x-ray datasets from sources like Kaggle, encompassing Normal, Tuberculosis, Covid-19, Pneumonia, are manually combined to create a unified dataset.
3. EXISTING SYSTEM Existing models individually predict diseases, but our aim is to develop a single model for predicting multiple lung diseases. In the past, separate models were utilized for each lung disease, but now we plan to consolidate them into a combined model. We employed deep learning, specifically convolutional neural network (CNN) analysis, to detect and classify chronic obstructive pulmonary disease (COPD) while
|
ISO 9001:2008 Certified Journal
|
Page 694