Dense Convolutional Neural Network for Detection of Cancer from CT Images.

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 computer...

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Detalles Bibliográficos
Publicado en:	BioMed Research International pp. 1 - 9
Autores principales:	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
Formato:	equations & formulas research tables/charts Journal Article
Publicado:	Wiley-Blackwell 6/20/2022
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	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.