3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

et al. 2022

Life

Self Cite

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.

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Yunus

et al. 2022

Life

Self Cite

“…Efficient classification based on automatic feature extraction is prepared by a convolutional neural network [22,[45][46][47][48][49][50]. Deep learning [19,20,43,48,51,52] methods are pre-trained models like AlexNet, GoogleNet [53], ResNet [54], VGG-16 [33], Inception V3 [55], and many others are used as feature extractors and feature selector. Ahmed et al suggested a method of WBCs feature extraction using a powerful CNN architecture VGGNet, extracted features are then filtered by the simulation of electron spectra for surface analysis (SESSA) algorithm [56].…”

Section: Related Workmentioning

confidence: 99%

A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models

Saleem

et al. 2021

Complex Intell. Syst.

Self Cite

White blood cells (WBCs) are a portion of the immune system which fights against germs. Leukemia is the most common blood cancer which may lead to death. It occurs due to the production of a large number of immature WBCs in the bone marrow that destroy healthy cells. To overcome the severity of this disease, it is necessary to diagnose the shapes of immature cells at an early stage that ultimately reduces the modality rate of the patients. Recently different types of segmentation and classification methods are presented based upon deep-learning (DL) models but still have some limitations. This research aims to propose a modified DL approach for the accurate segmentation of leukocytes and their classification. The proposed technique includes two core steps: preprocessing-based classification and segmentation. In preprocessing, synthetic images are generated using a generative adversarial network (GAN) and normalized by color transformation. The optimal deep features are extracted from each blood smear image using pretrained deep models i.e., DarkNet-53 and ShuffleNet. More informative features are selected by principal component analysis (PCA) and fused serially for classification. The morphological operations based on color thresholding with the deep semantic method are utilized for leukemia segmentation of classified cells. The classification accuracy achieved with ALL-IDB and LISC dataset is 100% and 99.70% for the classification of leukocytes i.e., blast, no blast, basophils, neutrophils, eosinophils, lymphocytes, and monocytes, respectively. Whereas semantic segmentation achieved 99.10% and 98.60% for average and global accuracy, respectively. The proposed method achieved outstanding outcomes as compared to the latest existing research works.

“… 25 , 26 , 27 , 28 , 29 Using computer‐aided diagnostic systems high accuracy of disease detection has been achieved. 30 , 31 , 32 , 33 Machine learning (ML)‐based diagnosis mechanisms are gaining increased attention in the many fields. 34 , 35 , 36 , 37 , 38 Medical diagnostic systems are being automated with help of deep learning (DL) that is an advanced version of ML and is a subset of AI.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advancements in computing technology, specifically in artificial intelligence (AI) and medical image processing, make it possible to detect many diseases automatically 25–29 . Using computer‐aided diagnostic systems high accuracy of disease detection has been achieved 30–33 . Machine learning (ML)‐based diagnosis mechanisms are gaining increased attention in the many fields 34–38 .…”

Section: Introductionmentioning

confidence: 99%

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

Umer

Concurrency and Computation

et al. 2021

Self Cite

Summary COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.