Quick and reliable diagnosis of stomach cancer by artificial neural network

被引:0
作者
Afshar, Saeid [1 ]
Abdolrahmani, Fahime [2 ]
Tanha, Fereshte Vakili [2 ]
Seaf, Mahin Zohdi [2 ]
Taheri, Kobra [2 ]
机构
[1] Tarbiat Modares Univ, Fac Sci, Dept Biophys & Biochem, Tehran, Iran
[2] PNU, Hamadan, Iran
来源
PROCEEDINGS OF THE 2ND WSEAS INTERNATIONAL CONFERENCE ON BIOMEDICAL ELECTRONICS AND BIOMEDICAL INFORMATICS: RECENT ADVANCES IN BIOMEDICAL ELECTRONICS AND BIOMEDICAL INFORMATICS | 2009年
关键词
Cancer; stomach; ANN; Artificial Neural Network; Training; SPSS; Matlab;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Approximately 90% of stomach cancers are adenocarcinoma that directly distribute from stomach wall to the neighbor tissue of stomach this kind of cancer is more common in persons who are more than 40 and its spread in men is twice as much as women. Unfortunately, this cancer doesn't have any sign until it develops to its advanced level. Generally biopsy, endoscopy, laparoscopy, ultra sonography, CT scan, x-ray radiography and proper clinical test are applied for cancer detection. By advancing artificial neural network technique and due to the difficulty in detection of stomach cancer with clinical and medicinal parameters, we decided to apply ANN for quick detection of stomach cancer. To do so, we use 50 clinical and medicinal parameters taken from 126 person (90 had cancer and 36 was normal as a testifier). We carried out independent sample T-Test with SPSS software for 50 parameters. Regard to the results of this analysis we selected 8 parameters that had lowest sig for ANN analysis (among parameters whose sig was less than 0.05). These parameters are age, anorexia, weight reduction, MCH MCHC, Na+ reduction, Ca2+ reduction and Xray radiography. Selected parameters of 126 persons split to three groups with Matlab software: training group (80%), validation group (10%), and test group (10%). Artificial neural network that we designed has three layers, 8 neurons as input, 8 neurons as hidden and I neuron as output. Split data are applied for training network with Levenberg-Marquardt learning algorithm. Finally, Performance of learning was 0.056, Regression coefficient between the output of trained network for test data and real results of test data was 0.927 and the area under ROC curve was 0.883. With these results we can conclude that training process was done successfully and accurately.
引用
收藏
页码:28 / +
页数:3
相关论文
共 50 条
[41]   Artificial Neural Network in Diagnosis of Urothelial Cell Carcinoma in Urine Cytology [J].
Muralidaran, Chandrasekaran ;
Dey, Pranab ;
Nijhawan, Raje ;
Kakkar, Nandita .
DIAGNOSTIC CYTOPATHOLOGY, 2015, 43 (06) :443-449
[42]   Construction and Validation of Artificial Neural Network Model Suggesting Nursing Diagnosis [J].
Nishi, Ryota ;
Kashiwagi, Kimikazu ;
Yokota, Shinichiroh ;
Ishii, Masamichi ;
Miyo, Kengo .
CIN-COMPUTERS INFORMATICS NURSING, 2025, 43 (06)
[43]   Application of psychoacoustics for gear fault diagnosis using artificial neural network [J].
Kane, P. V. ;
Andhare, A. B. .
JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL, 2016, 35 (03) :207-220
[44]   A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis [J].
She, Jiajie ;
Su, Danna ;
Diao, Ruiying ;
Wang, Liping .
FRONTIERS IN GENETICS, 2022, 13
[45]   Leak diagnosis in pipelines using a combined artificial neural network approach [J].
Perez-Perez, E. J. ;
Lopez-Estrada, F. R. ;
Valencia-Palomo, G. ;
Torres, L. ;
Puig, V ;
Mina-Antonio, J. D. .
CONTROL ENGINEERING PRACTICE, 2021, 107
[46]   Fault diagnosis in deregulated distribution systems using an artificial neural network [J].
Bretas, AS ;
Hadjsaid, N .
2001 IEEE POWER ENGINEERING SOCIETY WINTER MEETING, CONFERENCE PROCEEDINGS, VOLS 1-3, 2001, :821-823
[47]   Multiple Faults Diagnosis of Induction Motor Using Artificial Neural Network [J].
Jigyasu, Rajvardhan ;
Mathew, Lini ;
Sharma, Amandeep .
ADVANCED INFORMATICS FOR COMPUTING RESEARCH, ICAICR 2018, PT I, 2019, 955 :701-710
[48]   Melanoma Diagnosis from Dermoscopy Images Using Artificial Neural Network [J].
Majumder, Sharmin ;
Ullah, Muhammad Ahsan ;
Dhar, Jitu Prakash .
2019 5TH INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL ENGINEERING (ICAEE), 2019, :855-859
[49]   Fault diagnosis of rolling element bearing based on artificial neural network [J].
Gunerkar, Rohit S. ;
Jalan, Arun Kumar ;
Belgamwar, Sachin U. .
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2019, 33 (02) :505-511
[50]   Fault diagnosis of rolling element bearing based on artificial neural network [J].
Rohit S. Gunerkar ;
Arun Kumar Jalan ;
Sachin U Belgamwar .
Journal of Mechanical Science and Technology, 2019, 33 :505-511