Classification of OME with Eardrum Otoendoscopic Images Using Hybrid-Based Deep Models, NCA, and Gaussian Method

Bingol, Harun

doi:10.18280/ts.390422

Cited by 6 publications

(6 citation statements)

References 36 publications

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“…Harun Bingol [ 59 ] proposed a hybrid-based deep learning model for classifying Otitis Media with Effusion (OME) based on eardrum otoendoscopic images. The proposed model combined Neighborhood Component Analysis (NCA) and the Gaussian method to extract and select features.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental results on a dataset comprising 910 images indicated that the proposed model achieved a high accuracy of 94.8%. Harun Bingol [ 59 ] presented a novel approach for classifying cervical cancer on Gauss-enhanced Pap-smear images using a hybrid CNN model. The performance of the proposed model was tested on a dataset comprising 1000 images, and it was found to achieve an accuracy of 93.6%, which is better than that of various other existing methods.…”

Section: Related Workmentioning

confidence: 99%

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Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Mohamed,

Oyelade,

Ezugwu

2023

PLoS ONE

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Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Mohamed,

Oyelade,

Ezugwu

2023

PLoS ONE

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“…There exist several issues that arise in farming because of several environmental aspects and this disease in plant leaves is termed to be a strong aspect that causes deficiency in the quality of agricultural products. The aim is to alleviate this problem with machine learning (ML) models ( Bayram, Bingol & Alatas, 2022 ; Bingöl, 2022a , 2022b ). Several ML and segmentation models are devised for the categorization and discovery of diseases in plants through leaf images ( Subramanian et al, 2022 ; Krishnamoorthy et al, 2021 ; Sathishkumar et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning

Chilakalapudi,

Jayachandran

2024

PeerJ Computer Science

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Agriculture is imperative research in visual detection through computers. Here, the disease in plants can distress the quality and cultivation of farming. Earlier detection of disease lessens economic losses and provides better crop yield. Detection of disease from crops manually is an expensive and time-consuming task. A new scheme is devised for accomplishing multi-classification of disease using plant leaf images considering the chronological Flamingo search algorithm (CFSA) with transfer learning (TL). The leaf image undergoes pre-processing using Adaptive Anisotropic diffusion to discard noise. Here, the segmentation of plant leaf is done with U-Net++, and trained by the Moving Gorilla Remora algorithm (MGRA). The image augmentation is further applied considering two techniques namely position augmentation and color augmentation to reduce data dimensionality. Thereafter the feature mining is done to produce crucial features. Next, the classification in terms of the first level is considered for classifying plant type and classification in terms of the second level is done to categorize disease using convolutional neural network (CNN)-based TL with LeNet and it undergoes training using CFSA. The CFSA-TL-based CNN with LeNet provided better accuracy of 95.7%, sensitivity of 96.5% and specificity of 94.7%. Thus, this model is better for earlier plant leaf disease detection.

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“…The most important advantages are that it allows the early diagnosis of the disease, increasing the chance of treatment of the patient, as well as getting rid of the treatment costs, which are highly likely to occur in the advanced stages of the disease. Deep architectures have been frequently used in the biomedical field, especially in recent years 3–6 . In addition, another important advantage is to provide pre‐diagnosis to sick people in underdeveloped regions where there are no specialists or the number of specialists is insufficient, thanks to these technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Deep architectures have been frequently used in the biomedical field, especially in recent years. [3][4][5][6] In addition, another important advantage is to provide pre-diagnosis to sick people in underdeveloped regions where there are no specialists or the number of specialists is insufficient, thanks to these technologies.…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, RelieF, and gradient‐weighted class activation mapping based deep hybrid model

Bugday

Akçiçek

Bingol

et al. 2023

Int J Imaging Syst Tech

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Urinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal damage in the kidneys. In the study, a hybrid model was developed to detect hydronephrosis and ureteral stones from kidney images. In the developed model, heat maps of the original images were obtained by using gradient‐weighted class activation mapping (Grad‐CAM) technology. Then, feature maps were extracted from both the original and heatmap datasets using the Efficientnetb0 architecture. Extracted feature maps were concatenated using a multimodal fusion technique. In this way, different features of an image are obtained. This has a positive effect on the performance of the model. The Relief dimension reduction technique was used to eliminate unnecessary features in the obtained feature map so that the proposed model can work faster and more effectively. Finally, the optimized feature map is classified in the support vector machine (SVM) classifier. To compare the performance of the proposed hybrid model, results were obtained with 8 state‐of‐the‐art models accepted in the literature. Among these models, the highest accuracy value was achieved in the Efficientnetb0 architecture with 67.98%, whereas the accuracy of the proposed hybrid model was 91.1%. This value indicates that the proposed model can be used for HUN diagnosis.

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Classification of OME with Eardrum Otoendoscopic Images Using Hybrid-Based Deep Models, NCA, and Gaussian Method

Cited by 6 publications

References 36 publications

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning

Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, RelieF, and gradient‐weighted class activation mapping based deep hybrid model

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