Dense Convolutional Neural Network for Detection of Cancer from CT Images

被引:0
|
作者
Sreenivasu, S. V. N. [1 ]
Gomathi, S. [2 ]
Kumar, M. Jogendra [3 ]
Prathap, Lavanya [4 ]
Madduri, Abhishek [5 ]
Almutairi, Khalid M. A. [6 ]
Alonazi, Wadi B. [7 ]
Kali, D. [8 ]
Jayadhas, S. Arockia [9 ]
机构
[1] Narasaraopeta Engn Coll, Dept Comp Sci & Engn, Narasaraopeta 522601, Andhra Pradesh, India
[2] Sri Sairam Engn Coll, Dept Informat Technol, Chennai 602109, Tamil Nadu, India
[3] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram 522502, Andhra Pradesh, India
[4] Saveetha Inst Med & Tech Sci, Saveetha Dent Coll & Hosp, Dept Anat, Chennai 600077, Tamil Nadu, India
[5] Duke Univ, Dept Engn Management, Durham, NC 27708 USA
[6] King Saud Univ, Coll Appl Med Sci, Dept Commun Hlth Sci, POB 10219, Riyadh 11433, Saudi Arabia
[7] King Saud Univ, Coll Business Adm, Hlth Adm Dept, POB 71115, Riyadh 11587, Saudi Arabia
[8] Ryerson Univ, Dept Mech Engn, Toronto, ON, Canada
[9] St Joseph Univ, Dept EECE, Dar Es Salaam, Tanzania
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中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
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页数:8
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