Dense Convolutional Neural Network for Detection of Cancer from CT Images. (20th June 2022)

• Record Type: Journal Article
• Authors: Sreenivasu, S. V. N.; Gomathi, S.; Kumar, M. Jogendra; Prathap, Lavanya; Madduri, Abhishek; Almutairi, Khalid M. A.; Alonazi, Wadi B.; Kali, D.; Jayadhas, S. Arockia
• Other Names: Teekaraman Yuvaraja Academic Editor.
• Is Part Of: BioMed research international. Volume 2022(2022)
• Publication Date: 2022-06-20
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2022/1293548
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.