Invariant Feature-Based Darknet Architecture for Moving Object Classification

Vasavi, S.; Priyadarshini, Neha; Harshavaradhan, Koneru

doi:10.1109/jsen.2020.3007883

Cited by 39 publications

(11 citation statements)

References 42 publications

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“…The number of layers for this block increases for later stages of DarkNet. Figure 10 shows the detailed structure of the DarkNet neural network (Ma et al, 2020; Štancel & Hulič, 2019; Vasavi et al, 2020).…”

Section: Proposed Methodologymentioning

confidence: 99%

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Akbar

Hassan

Shoukat

et al. 2022

Microscopy Res & Technique

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Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.

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Section: Proposed Methodologymentioning

confidence: 99%

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Akbar

Hassan

Shoukat

et al. 2022

Microscopy Res & Technique

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“…This model takes input images in 256 × 256 size. Darknet53 is often used in object detection as the basis for YOLO 25 …”

Section: Methodsmentioning

confidence: 99%

“…Darknet53 is often used in object detection as the basis for YOLO. 25 The features obtained in the proposed model have been classified in the support vector machines (SVM) 26 classifier. In addition, decision trees (DT), 27 k-Nearest Neighbors (KNN), 28 Naive Bayes (NB), 29 subspace ensemble (SE), 30 and discriminant analysis (DA) 31 classifiers were used to measure the performance values of other classifiers.…”

Section: Deep Models Classifiers and Ncamentioning

confidence: 99%

Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid‐based deep models

Eroğlu

Yıldırım

2021

Int J Imaging Syst Tech

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Otitis media with effusion (OME) is fluid accumulation in the middle ear without signs of systemic infection. OME can cause hearing loss, ear fullness, speech retardation, and a decrease in social relations and school success. In the treatment of OME, methods such as medical treatment and placing a tympanostomy tube in the eardrum are used. Correct evaluation of the eardrum is necessary to diagnose OME and follow‐up properly in the following period. In this study, we aimed to evaluate the otoendoscopic images of the eardrum in patients with otitis media with effusion using deep models. In the proposed model, Efficientnetb0, Darknet53, and Densenet201 architectures are used as the base. The feature maps obtained using these architectures are combined. In this way, different features of the otoendoscopic images of the eardrum in the data set were extracted and combined using different architectures. neighborhood component analysis optimization technique is used to select important features from the combined feature maps. By using this optimization method, the size of feature maps is reduced. In this way, the model is trained faster, and the testing process is carried out in a shorter time. When these features are classified in different classifiers, the model we propose has the most successful average accuracy value of 94.27% in the SVM classifier.

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“…DL methods are more precise as compared to the approaches focusing on handcrafted features. In medical imaging, several models have been developed such as Alexnet, VGG19 [ 58 ], Darknet [ 59 ], etc., for the extraction of features. Kevin et al developed a model for OA diagnosis and total knee replacement by using DL model Resnet-34 [ 60 ] which has 34 layers.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

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Invariant Feature-Based Darknet Architecture for Moving Object Classification

Cited by 39 publications

References 42 publications

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid‐based deep models

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

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