Enhancing Prostate Cancer Diagnosis with Multi-class Classification of CT Scan Images
- Title
- Enhancing Prostate Cancer Diagnosis with Multi-class Classification of CT Scan Images
- Abstract
- This work intends to develop a multi-class classification system for prostate cancer prediction utilising CT scan data in order to enhance prostate cancer diagnosis and treatment planning, and hence patient outcomes. To begin, a preprocessing phase in this research eliminated noise and artefacts from the CT images. Following that, we extracted features using a hybrid approach by mixing deep learning with classic image processing techniques. Following the retrieval of features, they were used to train a convolutional neural network (CNN) multi-class classification model. The technique's efficiency was evaluated experimentally using a collection of CT images from males diagnosed with prostate cancer. The results demonstrated that the proposed multi-class classification approach was very accurate in predicting the existence of prostate cancer as well as the stage of the disease. In conclusion, our work shows that multi-class classification models may be used to predict prostate cancer using CT scan pictures. The described strategy has the potential to significantly increase both the precision with which prostate cancer is identified and the efficacy with which treatment strategies are devised.
- Scientific Type
- غير معروف
- Collaboration type
- مشترك
- Publish Date
- May 28, 2023
- Participated Universities (Publication)
-
Alnoor University
- Scopus status
- In Scopus
- Scopus index year
- 2 023
- Scopus quarter
- 1
- Scopus citation score
- 73.19999695
- Clarivate status
- Not In Clarivate
- Pub. Med. status
- Not In PubMed
- Author (Publication)
- زهراء نافع بدر محمد
- Journal (Publication)
- IEEE-2023 Third International Conference on Secure Cyber Computing and Communication
- Publisher (Publication)
-
IEEE Xplore
- ISSN
- 1553-877X
- Country (Publication)
-
United kingdom
- Country type
- عالمية
- College (Publication)
-
College of Health and Medical Technologies
- Departement (Publication)
-
Department of Radiology
Part of Enhancing Prostate Cancer Diagnosis with Multi-class Classification of CT Scan Images