Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network

Başaran, Erdal; Cömert, Zafer; Şengür, Abdulkadir; Budak, Ümit; Çelik, Yüksel; Toğaçar, Mesut

doi:10.1109/ubmk.2019.8907070

Cited by 19 publications

(14 citation statements)

References 8 publications

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“…For this purpose, digital image processing techniques such as local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) have been used. These systems have been supported using traditional machine learning algorithms (Basaran et al, 2019a). Moreover, in some related studies, it has been observed that segmentation methods have been recommended to focus on TM annulus (Başaran, Cömert & Çelik, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, in some related studies, it has been observed that segmentation methods have been recommended to focus on TM annulus (Başaran, Cömert & Çelik, 2020). Depending on the advances in deep learning, CNNs have been adopted to the OM diagnosis task (Basaran et al, 2019a(Basaran et al, , 2019bBaşaran, Cömert & Çelik, 2020). In the training of the CNNs, both the transfer learning (Cha et al, 2019) and training from scratch (Lee, Choi & Chung, 2019) approaches have been performed.…”

Section: Discussionmentioning

confidence: 99%

“…The raw otoscope images were input to the AlexNet in this study. To validate the model, 10-fold cross-validation was also used and the model ensured an accuracy of 98.77% (Basaran et al, 2019a). A computational model relying upon the Faster R-CNN and pretrained convolutional neural networks (CNNs) was introduced for separating normal and abnormal TMs.…”

Section: Introductionmentioning

confidence: 99%

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Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Alhudhaif

Cömert

Polat

2021

PeerJ Computer Science

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Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Alhudhaif

Cömert

Polat

2021

PeerJ Computer Science

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“…The number of normal TM samples is 535 whereas the numbers of AOM, CSOM, Earwax samples are 119, 63 and 140 respectively. By the way, the samples belonging to otitis externa (41), ear ventilation tube (16), foreign bodies in the ear (3), pseudo-membranes (11), and tympanoskleros (28) were collected in the other class. Each otoscope sample in the database was evaluated by three ENT specialists.…”

Section: Eardrum Datasetmentioning

confidence: 99%

“…The raw otoscope images were input to the AlexNet in this study. To validate the model, 10-fold cross-validation was also used and the model ensured an accuracy of 98.77% [16]. A computational model relying upon the Faster R-CNN and pretrained convolutional neural networks (CNNs) was introduced for separating normal and abnormal TMs.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm (v0.1)"

2021

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Background: Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone.Methods: In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results:The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.

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Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Taylor,

Sheikh

2024

Comp Applic In Engineering

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Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.

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Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network

Cited by 19 publications

References 8 publications

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Peer Review #2 of "Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm (v0.1)"

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

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