Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.
An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in...
| Published in: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1460 - 1480 |
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| Main Authors: | , , , , |
| Format: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808822&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808822 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808822 163511582 169808822 169808822 10.1007/s10278-023-00828-7 169808822 ppf: 1460 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images. aug: au: Rumala, Dewinda Julianensi van Ooijen, Peter Rachmadi, Reza Fuad Sensusiati, Anggraini Dwi Purnama, I Ketut Eddy affil: Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia sug: subj: Brain Diseases Diagnosis Convolutional Neural Networks Deep Learning Automation Diagnosis, Computer Assisted Algorithms Human Brain Diseases Classification Forecasting Prediction Models Brain Radiography Magnetic Resonance Imaging Diagnosis, Brain Brain Mapping Analysis of Variance Conceptual Framework Validity Outcomes (Health Care) Quality of Life ab: An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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