Detection of Lung Cancer with a Deep Learning Hybrid Classifier
- Title
- Detection of Lung Cancer with a Deep Learning Hybrid Classifier
- Creator
- Kamatagi P.; Jacob L.; Balagangadhar K.
- Description
- This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Artificial Intelligence (AI); Computer-Aided Diagnosis (CAD); Convolution Neural Networks (CNN); Deep Learning; Feature Selection; Healthcare Technology; Hybrid Classification; Image Classification; Image Preprocessing; Lung Cancer; Medical Diagnosis; Medical Imaging; Medical Research; SVM Hybrid Classifier; VGG-16
- Coverage
- Kamatagi P., Christ (Deemed to Be University), Department of Data Science, Lavasa, India; Jacob L., Christ (Deemed to Be University), Department of Data Science, Lavasa, India; Balagangadhar K., Christ (Deemed to Be University), Department of Data Science, Lavasa, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kamatagi P.; Jacob L.; Balagangadhar K., “Detection of Lung Cancer with a Deep Learning Hybrid Classifier,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19169.