Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions

Amin, Javeria; Anjum, Muhammad Almas; Sharif, Muhammad

doi:10.1016/j.knosys.2022.108881

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…This combination is used to detect other types of cancer such as breast cancer, etc. [ 39 , 40 , 41 ], and in other applications, such as those presented in [ 42 ]. Second , we combined VGG-16 [ 43 ] with ResNet-50V2 [ 44 ], and U-Net [ 45 ] with LSTM [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

Othman

Mahmoud

Dhahri

et al. 2022

Sensors

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Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.

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Section: Methodsmentioning

confidence: 99%

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

Othman

Mahmoud

Dhahri

et al. 2022

Sensors

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“…It is assumed that there is a twodimensional dataset, and one of the data sample points 𝑋 is taken from it. The coordinates for this point in the dataset are (8,4). A random value of 0.5 has been assigned to the 𝑟𝑎𝑛𝑑 (0,1) variable, and the coordinates of a nearby sample point of 𝑋 have been assigned the values (2,6).…”

Section: Data Balancing Smotementioning

confidence: 99%

“…The training process involves feeding the algorithms with labelled images, where the ground truth diagnosis is known, and the algorithms adjust their internal parameters to optimize their performance. Once trained, these machine learning models can be deployed on edge devices or cloud platforms, enabling real-time classification of retinal images [8]. Edge devices such as smartphones or portable devices equipped with image sensors can analyse retinal images on-site, without the need for a stable internet connection.…”

Section: Introductionmentioning

confidence: 99%

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

2023

IJIES

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Diabetic retinopathy (DR) is a prevalent and potentially blinding eye disease that affects individuals with diabetes. On the other hand, internet of medical things (IoMT) incorporation into DR grading classification offers a promising future for the field of eye health. It optimizes the diagnostic process, increases accessibility to healthcare services, and ultimately improves patient care and outcomes. Therefore, accurate and timely grading of DR severity is crucial for effective disease management. In this article, DR-grading network (DRG-Net) is proposed, which is a comprehensive approach for DR labels classification using the indian diabetic retinopathy image dataset (IDRiD) dataset. To address the imbalanced nature of the dataset, synthetic minority over-sampling technique (SMOTE) is employed for data balancing, ensuring representative samples for each severity level. Then, transfer learning based residual network-50 (ResNet50) architecture is used to extract features from SMOTE outcome, which is a deep learning model renowned for its ability to learn complex image representations. Finally, graph-based K-nearest neighbours (GKNN) classification which utilizes the spatial relationships between samples to make informed decisions, considering the similarity of retinal images in a graph-based representation is introduced for enhanced grading classification of DR. The simulation results show that, the proposed DRG-Net resulted in improved performance as compared to state-of-the-art approaches such as MSA-ResNetGB, DLCNN-MGWO-VW, OHGCNet, and E-DenseNet BC-121 with an accuracy of 99.93%, and F1-score of 99.85%.

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“…There has been plenty of work carried out in the area of KOA imaging to identify and classify knee diseases. In image processing, feature extraction is an effective step for image representation [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. For the recognition of diseases, feature extraction is very helpful to machine learning (ML) algorithms.…”

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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Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions

Cited by 16 publications

References 26 publications

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

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

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