Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder.
Hearing loss, a partial or total inability to hear, is known as hearing impairment. Untreated hearing loss can have a bad effect on normal social communication, and it can cause psychological problems in patients. Therefore, we design a three-category classification system to detect the specific cat...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 10; pp. 1 - 12 |
|---|---|
| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125425151&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125425151 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2017 vid: 41 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125425151 125425151 125425151 10.1007/s10916-017-0814-4 125425151 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder. aug: au: Jia, Wenjuan Yang, Ming Wang, Shui-Hua affil: School of Computer Science and Engineering , Nanjing Normal University , Wenyuan Nanjing 210023 People's Republic of China sug: subj: Magnetic Resonance Imaging Neural Networks (Computer) Hearing Disorders Classification Image Processing, Computer Assisted Autoencoder Human Experimental Studies Funding Source Algorithms ab: Hearing loss, a partial or total inability to hear, is known as hearing impairment. Untreated hearing loss can have a bad effect on normal social communication, and it can cause psychological problems in patients. Therefore, we design a three-category classification system to detect the specific category of hearing loss, which is beneficial to be treated in time for patients. Before the training and test stages, we use the technology of data augmentation to produce a balanced dataset. Then we use deep autoencoder neural network to classify the magnetic resonance brain images. In the stage of deep autoencoder, we use stacked sparse autoencoder to generate visual features, and softmax layer to classify the different brain images into three categories of hearing loss. Our method can obtain good experimental results. The overall accuracy of our method is 99.5%, and the time consuming is 0.078 s per brain image. Our proposed method based on stacked sparse autoencoder works well in classification of hearing loss images. The overall accuracy of our method is 4% higher than the best of state-of-the-art approaches. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|