A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
- Title
- A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
- Creator
- Dominic D.; Balachandran K.
- Description
- Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
- Source
- Lecture Notes in Networks and Systems, Vol-132, pp. 107-114.
- Date
- 2021-01-01
- Publisher
- Springer
- Subject
- Deep learning; DiCoM images; Image enhancement; ZCA whitening. LBP
- Coverage
- Dominic D., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India; Balachandran K., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Dominic D.; Balachandran K., “A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18792.