A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
- Title
- A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
- Creator
- Mukku L.; Thomas J.
- Description
- Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-868, pp. 63-75.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Cervical cancer; Deep learning; Expectation maximization; Gaussian mixture modelling; Lesion feature extraction; Segmentation
- Coverage
- Mukku L., CHRIST (Deemed to be University) Kengeri, Bangalore, 560074, India; Thomas J., CHRIST (Deemed to be University) Kengeri, Bangalore, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981999036-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mukku L.; Thomas J., “A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19511.