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Recently, many diseases have negatively impacted people’s lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people’s quality of life. Full article

Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review. Full article

Two novel low band gap donor–acceptor (D–A) copolymers, poly[9,10-bis(4-(dodecyloxy)phenyl)-2,6-anthracene-alt-5,5-(4′,7′-bis(2-thienyl)-2′,1′,3′-benzothiadiazole-N-5,6-(3,7-dimethyloctyl)dicarboxylic imide)] (PPADTBTDI-DMO) and poly[9,10-bis(4-(dodecyloxy)phenyl)-2,6-anthracene-alt-5,5-(4′,7′-bis(2-thienyl)-2′,1′,3′-benzothiadiazole-5,6-N-octyl-dicarboxylic imide)] (PPADTBTDI-8) were synthesized in the present work by copolymerising the bis-boronate ester of 9,10-phenylsubstituted anthracene flanked by thienyl groups as electron–donor units with benzothiadiazole dicarboxylic imide (BTDI) as electron–acceptor units. Both polymers were synthesized in good yields via Suzuki polymerisation. Two different solubilizing alkyl chains were anchored to the BTDI units in order to investigate the impact upon their solubilities, molecular weights, optical and electrochemical properties, structural properties and thermal stability of the resulting polymers. Both polymers have comparable molecular weights and have a low optical band gap (E_g) of 1.66 eV. The polymers have low-lying highest occupied molecular orbital (HOMO) levels of about −5.5 eV as well as the similar lowest unoccupied molecular orbital (LUMO) energy levels of −3.56 eV. Thermogravimetric analyses (TGA) of PPADTBTDI-DMO and PPADTBTDI-8 did not prove instability with decomposition temperatures at 354 and 313 °C, respectively. Powder X-ray diffraction (XRD) studies have shown that both polymers have an amorphous nature in the solid state, which could be used as electrolytes in optoelectronic devices. Full article