WITHDRAWN: Gait diagnosis using fuzzy logic with wearable tech for prolonged disorders of diabetic cardiomyopathy

Sharma, Neha; Murthy, A. Sampath Dakshina; Karthikeyan, T.; Kumari, Ch. Usha; Jagan, B. Omkar Lakshmi

doi:10.1016/j.matpr.2020.10.623

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…Preprocessing techniques remove unnecessary elements such as RBCs, noise, and unwanted features. 26 The remaining features in the image show the blasts generated by the abnormal WBCs if the image belongs to a cancerous blood sample.…”

Section: Introductionmentioning

confidence: 99%

“…Image processing enables the machine to diagnose the type of blood leukemia based on the characteristics of the blood cells. Preprocessing techniques remove unnecessary elements such as RBCs, noise, and unwanted features 26 . The remaining features in the image show the blasts generated by the abnormal WBCs if the image belongs to a cancerous blood sample.…”

Section: Introductionmentioning

confidence: 99%

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Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

Aydin Atasoy,

Faris Abdulla Al Rahhawi

2024

Int J Imaging Syst Tech

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The automatic detection of bone marrow (BM) cell diseases plays a vital role in the medical field; it helps to make diagnoses more precise and effective, which leads to early detection and can significantly improve patient outcomes and increase the chances of successful intervention. This study proposed a fully automated intelligent system for BM classification by developing and enhancing Capsule Neural Network (CapsNet) architecture. Although CapsNet has demonstrated success in many classification tasks, it still has some limitations and challenges associated with using Convolutional Neural Networks (CNNs), which suffer from information loss during the pooling and discarding of detailed spatial information, resulting in the loss of fine‐grained features. Additionally, CNNs must help capture hierarchical feature relationships and often learn them implicitly by stacking convolutional layers. In contrast, CapsNets are designed to capture hierarchical features through dynamic routing and relationships between capsules, resulting in a more explicit representation of spatial hierarchy. CapsNets manage transformations and offer equivariance, preserving spatial information through capsule routing mechanisms. Further, to improve how features are represented, pre‐trained models such as Residual Capsule Network (RES‐CapsNet), Visual Geometry Group Capsule Network (VGG‐CapsNet), and Google Network (Inception V3) (GN‐CapsNet) have been used. This helps the network obtain the low‐ and mid‐level features and information it has previously learned so that subsequent capsule layers receive better initial information. Additionally, the Synthetic Minority Over‐Sampling Technique (SMOTE) was implemented to mitigate class imbalance. It generates synthetic samples in feature space by over‐sampling the minority class, leading to improving model performance in accurately classifying rare instances. Fine‐tuning the hyperparameters and implementing these improvements resulted in remarkable accuracy rates on a large BM dataset, with reduced training time and trainable parameters. CapsNet achieved 96.99%, VGG‐CapsNet achieved 98.95%, RES‐CapsNet achieved 99.24%, and the GN‐CapsNet model demonstrated superior accuracy at 99.45%. GN‐Caps Net was the best because it requires a small number of epochs and has an effective deep inception architecture that efficiently extracts features at different scales to form a robust representation of the input. Our proposed models were compared with existing state‐of‐the‐art models using the BM dataset; the results showed that our models outperformed the existing approaches and demonstrated excellent performance. Further, this automated system can analyze large amounts of data and complex cells in images of the BM dataset. Thus, it gives healthcare professionals a detailed understanding of different diseases, which may take time to achieve manually.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

Aydin Atasoy,

Faris Abdulla Al Rahhawi

2024

Int J Imaging Syst Tech

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“…Throughout its useful life, a building's exterior faces deterioration processes due to the action of many external agents, including organic compounds, which affect its physical and chemical properties [1]. The great exposure of these surfaces to external actions leads to the appearance of anomalies, which can affect their decorative functions, as well as jeopardizing their durability and performance.…”

Section: Introductionmentioning

confidence: 99%

Assessment of self-cleaning properties on concrete façade panels

Lessa,

Lima,

Margalho

et al. 2024

Preprint

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Building façades are affected by atmospheric pollution and other external agents that can impact aesthetic aspects and lead to the appearance of anomalies with impacts on the durability and performance of materials. The reduction in maintenance costs and the preservation of the original characteristics of coatings make the use of photocatalytic semiconductor materials highly attractive, particularly for the conservation of historical heritage. These materials can maintain the cleanliness of building surfaces, promote the degradation of pollutants, and have decontamination and antibacterial effects. Thus, the objective of this study was to develop and evaluate the self-cleaning properties of cementitious panels for façades, aiming to obtain a coating with characteristics to contribute to the preservation of built heritage through the incorporation of titanium dioxide (TiO2) nanoparticles and zinc oxide (ZnO) microparticles. To evaluate the self-cleaning, a Rhodamine B (RhB) solution was deposited on the surfaces of concrete samples, which were subjected to 5 irradiation phases by a lamp simulating sunlight totalling 720 minutes. Visual analysis and colour characterisation tests were carried out to evaluate the self-cleaning capacity. The results showed that the treatment improved surface cleanliness after exposure to light while preserving the original aesthetics of the surfaces.

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