Swarm Optimized Modular Neural Network Based Diagnostic System for Breast Cancer Diagnosis

Pandey, Bipul; Garg, Nitish

doi:10.5121/ijscai.2013.2402

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The artificial neural network (ANN), which is based on the brain's neural structure (Rosenblatt, 1958 ), raised the interest of scientific community worldwide in the field of medicine due to its potential for diagnostic and prognostic applications (Smith et al, 1988 ; Salim, 2004 ; Kamruzzaman et al, 2010 ; Patil and Mudholkar, 2012 ). It has been used in heart disease (Kamruzzaman et al, 2010 ), predicting headache, pre-diagnosis of hypertension (Sumathi and Santhakumaran, 2011 ), kidney stone diseases (Kumar and Abhishek, 2012 ), classifying breast masses to identify breast cancer (Das and Bhattacharya, 2008 ; Pandey et al, 2012 ), dermatologist-level classification of skin diseases/cancer (Bakpo and Kabari, 2011 ; Esteva et al, 2017 ), prediction of skin cancer and blood cancer (Payandeh et al, 2009 ; Esteva et al, 2017 ; Roffman et al, 2018a ), and diagnosis of PC (Sanoob et al, 2016 ). As an example of the workflow in these applications, classification of skin cancer was performed via a single convolutional neural network, which was trained with a dataset of 129,450 clinical images (Esteva et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Pancreatic Cancer Prediction Through an Artificial Neural Network

Muhammad

Hart

Nartowt

et al. 2019

Front. Artif. Intell.

111

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Early detection of pancreatic cancer is challenging because cancer-specific symptoms occur only at an advanced stage, and a reliable screening tool to identify high-risk patients is lacking. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data of 800,114 respondents captured in the National Health Interview Survey (NHIS) and Pancreatic, Lung, Colorectal, and Ovarian cancer (PLCO) datasets, together containing 898 patients diagnosed with pancreatic cancer. Prediction of pancreatic cancer risk was assessed at an individual level by incorporating 18 features into the neural network. The established ANN model achieved a sensitivity of 87.3 and 80.7%, a specificity of 80.8 and 80.7%, and an area under the receiver operating characteristic curve of 0.86 and 0.85 for the training and testing cohorts, respectively. These results indicate that our ANN can be used to predict pancreatic cancer risk with high discriminatory power and may provide a novel approach to identify patients at higher risk for pancreatic cancer who may benefit from more tailored screening and intervention.

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Section: Introductionmentioning

confidence: 99%

Pancreatic Cancer Prediction Through an Artificial Neural Network

Muhammad

Hart

Nartowt

et al. 2019

Front. Artif. Intell.

111

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“…In 2012, Pandeyet al, [41] implemented an MNN approach combined with genetic algorithms to improve the diagnosis of breast cancer with high accuracy. The MNN consists of individual neural modules, each trained with GA using the training set.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

Egba, Anwaitu Fraser et al.

2024

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Artificial Neural Networks (ANNs) are a type of machine learning algorithms that are used to solve problems such as medical diagnosis. In recent times, the amount of data that is generated daily is on the increase and the level of the complexity of problems is troubling. ANN algorithms are commonly used to overcome these challenges are further faced with the problem of having fixed data attributes as a dataset for its input layer, the complexity of having heterogeneous datasets instead of homogeneous datasets,and having a single objective output layer instead of a multi-objective output layer that could enable the diagnosis of multiple diseases. This researchproposes an enhanced modular-based Neural Network algorithm that utilizes heterogeneous datasets drawn from multiple sources, decomposed and clustered into independent units, and then trained by ANNs selected according to their learning paradigms -supervised, unsupervised, and reinforcement learning, to provide an effective, efficient and timely medical diagnosis, especially in developing countries where modern facilities are lacking with much dependence on manual methods.Thus an integrated system with multiple ANN techniques modelled into a single unit is developed. The results show that the proposed approach has been significantly successful indealing with the aforesaid problem compared to other methods with a training accuracy of 0.905, Sensitivity of 0.917, and specificity of 0.923.

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“…(Ahmad, Muhammad, & Miller, 2012) used a fast learning neuroevolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN). (Pandey, Jain, Kothari, & Grover, 2012) used modular neural network instead of the traditional monolithic neural network. (Singh, Saini, & Singh, 2012) presented a system that uses unsupervised Adaptive Resonance Neural Network (ARNN).…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

Al-Khasawneh

2016

International Journal of E-Health and Medical Communications

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Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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Swarm Optimized Modular Neural Network Based Diagnostic System for Breast Cancer Diagnosis

Abstract: Abstract

Cited by 8 publications

References 17 publications

Pancreatic Cancer Prediction Through an Artificial Neural Network

Pancreatic Cancer Prediction Through an Artificial Neural Network

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

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