International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023
p-ISSN: 2395-0072
www.irjet.net
Enhancing Lung Cancer Detection with Deep Learning: A CT Image Classification Approach JEEVIKA K S1, DR. SAVITHA S K2 1PG Student, Dept. of Computer Science Engineering, Bangalore Institute of Technology, Bengaluru, India 2Professor, Dept. of Computer Science Engineering, Bangalore Institute of Technology, Bengaluru, India
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Abstract - Lung cancer is a highly perilous illness ranking
The second step is image segmentation, which detects the edge using Canny edge detection, and lung segmentation techniques using K-means clustering to separate the background and foreground. Morphological operations to refine the lung mask. The original image binary threshold image eroded, dilated color-labeled image-segmented lung mask, and segmented lung area in the original image are displayed. After the segmentation lung feature extraction process, which is not displayed in the system, the model directly underwent analysis assessment.
as one of the primary causes of disease and death, particularly when diagnosed in its initial stages. It presents significant challenges, as it is often only discernible after it has already diffused. This study proposes a lung cancer prognostication framework that uses deep learning to enhance the accuracy of cancer forecasting and disease determination, thereby enabling personalized treatment approaches based on disease severity. It consists of various steps, including image preprocessing and segmentation of lung CT image features extracted from the segmented images. Three different models, namely a DCNN model, a DCDNN model, and an ANN model, were employed for image classification, and a deep convolutional neural network (DCNN) was employed to detect lung diagnosis based on the extracted feature evaluation results showing the best accuracy of 99.41% in accurately discerning the presence or absence of lung cancer. The GAN model generates realistic lung CT scan images by training a generator to produce authentic images, and a discriminator to distinguish between real and fake images. The outcome of the system depends on the quality of the data, and a well-trained DCNN through training, validation, and testing on diverse datasets is crucial to ensure the reliability and generalizability of the model.
In the third step, the classification model was trained and evaluated. Collect a dataset of lung images with labels normal or cancer and split the dataset into three sets. In the evaluation process, it provides access to the training history to retrieve the training and validation accuracy, and the loss values are plotted using Matplotlib. The models used in this study were the DCNN, DCDNN, and ANN. Every model exhibited good accuracy and loss percentage and plotted graphs. Further details are provided in the following sections. In the fourth step, the GAN model predicts, the structure of the image batch, class names, labels, and filenames in this understood. During the ultimate step, the Streamlit app for lung cancer detection allows users to upload images and predict cancer. User-friendly interface that interacts with an application on a web browser.
Key Words: Lung cancer detection, Deep learning, Deep convolutional neural network.
1. INTRODUCTION
1.1 Aims and Objectives
Lung cancer is a complex and heterogeneous ailment characterized by a rise in the number of cells in lung tissues. This stands as the main reason for most cancer-related deaths worldwide, accounting for a sizable proportion of cancer-associated morbidity and mortality. Detection assumes a crucial role in identifying the existence of lung cancer at an early stage and facilitating timely treatment. This study focuses on the development and improvement of detection methods using different techniques.
1) The aim of this project is to enhance the precision and effectiveness of lung cancer diagnosis by advanced deep learning methodologies. 2) To develop an application that detects and properly classifies Lung cancer in CT scan images using a DCNN.
This study provides an input CT image for image preprocessing, which includes grayscale conversion for noise removal, histogram calculation, and image quality enhancement for more clearly visible images after image enhancement.
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