Real-Time Glaucoma Detection From Digital Fundus Images Using Self-ONNs

Devecioğlu, Özer Can; Malik, Junaid; İnce, Türker; Kıranyaz, Serkan; Atalay, Eray; Gabbouj, Moncef

doi:10.1109/access.2021.3118102

Cited by 42 publications

(16 citation statements)

References 34 publications

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“…Recently, operational neural networks (ONNs) have been applied as a diverse network model for image analyzing, classification, and processing due to their non-linear properties, low computational complexity, simplicity in structure, and high performances. A selforganized ONN (Self-ONN) model was proposed in [45,46] to classify the biomedical images. It is seen that the Self-ONN model can perform better than conventional CNN models if the model architecture and parameters can be tweaked carefully.…”

Section: Cmp-cnnmentioning

confidence: 99%

A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images

et al. 2023

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Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system.

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Section: Cmp-cnnmentioning

confidence: 99%

A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images

et al. 2023

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“…Recently, operational neural networks (ONNs) have been applied as a diverse network standard for image analyzing, classification, and processing due to their non-linear properties, low computational complexity, simplicity in structure, and high performance. A self-organized ONN (Self-ONN) model was proposed in [ 44 , 45 ] to classify the biomedical images. It is seen that the Self-ONN model can perform better than conventional CNN models if the model architecture and parameters can be tweaked carefully.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models

et al. 2023

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Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor’s pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.

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“…To further boost the restoration performance and reduce the complexity, operational Cycle-GANs are proposed in this study. Derived from Generalized Operational Perceptrons [10]- [15], Operational Neural Networks (ONNs) [16]- [18], and their new variants, Self-Organized Operational Neural Networks (Self-ONNs) [21], [22], [30]- [32], are heterogeneous network models with a non-linear neuron model. Self-ONNs are heterogeneous network models with a non-linear neuron model which have shown superior diversity and increased learning capabilities.…”

Section: Introductionmentioning

confidence: 99%