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13th International Conference on Computer and Knowledge Engineering
Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province
Authors :
Mohammad Ali Javadzadeh Barzaki
1
Jafar Abdollahi
2
Mohammad Negaresh
3
Maryam Salimi
4
Hadi Zolfeghari
5
Mohsen Mohammadi
6
Asma Salmani
7
Rona Jannati
8
Firouz Amani
9
1- Deputy of Research and Technology, Ardabil University of Medical Sciences Ardabil, Iran
2- Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
3- Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
4- Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
5- Department of Community Medicine, Faculty of Medicine, Ardabil University of Medical Science, Ardabil, Iran
6- Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
7- Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
8- Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
9- Department of Community Medicine, Faculty of Medicine, Ardabil University of Medical Science, Ardabil, Iran
Keywords :
Deep learning،Lung cancer،Classification،Xception
Abstract :
In the past, doctors had to physically locate the region they thought could have lung cancer. Several studies have shown that manual segmentation is time-consuming and dependent on the operator and instrument. To aid healthcare providers in making an early diagnosis of lung cancer, an algorithm known as the XCEPTION Based Classification for Lung Cancer (XCEPTION) has been created. XCEPTION enhances the categorization of lung cancer lesions by using a neural network to assist doctors in diagnosing lung cancer. Medical pictures of lung cancer in malignant, benign, and healthy people may all be classified by XCEPTION. Each and every ethical guideline has been followed while doing research for this study. This approach uses a convolutional neural network to categorize lung cancer in order to help medical practitioners identify lung cancer lesions. With precision and sensitivity, XCEPTION can identify incoming medical photos as belonging to cancer patients or healthy people.
Papers List
List of archived papers
Information Theoretic Learning-based Deep Embedded Clustering (ITL-DEC)
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Brain Age Estimation with Twin Vision Transformer using Hippocampus Information Applicable to Alzheimer Dementia Diagnosis
Zahra Qodrati - Seyedeh Masoumeh Taji - Amirhossein Ghaemi - Habibollah Danyali - Kamran Kazemi - Alireza Ghaemi
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition
Hamid Ahmadabadi - Omid Nejati Manzari - Ahmad Ayatollahi
MIPS-Core Application Specific Instruction-Set Processor for IDEA Cryptography − Comparison between Single-Cycle and Multi-Cycle Architectures
Ahmad Ahmadi - Reza Faghih Mirzaee
Graph-Theoretic Approach and Advanced Data Balancing for Liver Disease Diagnosis Improvement
Soheib Kiani - Sadegh Sulaimany
A Novel Approach for Image-Text Matching Cross-Modal Space Learning
Amirreza Ebrahimi - Mohammad Javad Parseh - Pejman Rasti
An Efficient Approach for Breast Abnormality Detection through High-Level Features of Thermography Images
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Improving the classification of high dimensional class-imbalanced data using the Chaos particle swarm optimization with Levy Flight
Mohammad Ali Zarif - Javad Hamidzadeh
Object Detection on Detecting Skin Lesion using Dab-DETR
Sheida Shadman - Amirreza Rouhbakhshmeghrazi - Shayan Nalbandian - Bo Li - Shaghayegh Shadman - Malik Muhammad Owais Siddique
Leveraging a structure-based and learning-based predictor using various feature groups in bioinformatics (case study: protein-peptide region residue-level interaction)
Shima Shafiee - Abdolhossein Fathi
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