A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. OBJECTIVE: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. METHODS: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). RESULTS: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. CONCLUSIONS: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.

Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%.

AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging.

Alzheimer's disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory and thinking skills of an individual. Accurate detection of AD has been a challenging research topic for a long time in the area of medical image processing. Detecting AD at its earliest stage is crucial for the successful treatment of the disease. The proposed Adaptive Hybrid Attention Network (AHANet) has two attention modules, namely Enhanced Non-Local Attention (ENLA) and Coordinate Attention. These modules extract global-level features and local-level features separately from the brain Magnetic Resonance Imaging (MRI), thereby boosting the feature extraction power of the network. The ENLA module extracts spatial and contextual information on a global scale while also capturing important long-range dependencies. The Coordinate Attention module captures local features from the input images. It embeds positional information into the channel attention mechanism for enhanced feature extraction. Moreover, an Adaptive Feature Aggregation (AFA) module is proposed to fuse features from the global and local levels in an effective way. As a result of incorporating the above architectural enhancements into the DenseNet architecture, the proposed network exhibited better performance compared to the existing works. The proposed network was trained and tested on the ADNI dataset, yielding a classification accuracy of 98.53%.

A deep learning network for Gleason grading of prostate biopsies using EfficientNet.

OBJECTIVES: The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work. METHODS: In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting. RESULT: To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset. CONCLUSIONS: The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images.

The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner's expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.

Efficacy and Challenges in the Treatment of Diastasis Recti Abdominis-A Scoping Review on the Current Trends and Future Perspectives.

Diastasis recti abdominis (DRA) is more prevalent in women during pregnancy and postpartum. However, there is a lack of awareness regarding this condition among women. The prevalence of DRA is high in late pregnancy and reduces during postpartum. The purpose of this study is to provide an overview of the treatment strategies for DRA and to discuss the significance of the technology towards better diagnosis and treatment. This work investigated 77 research articles published in the recognized research databases. The study aims to analyze the diagnostic and treatment procedures and the role of technology within them. The management strategy for DRA can either be conservative or surgical. Exercise therapy has been shown to improve functional impairments. These exercises focus on recruiting the abdominal muscles. Electromyography and Ultrasound imaging have been employed as useful tools in assessing the abdominal muscles effectively. This study has examined the treatment methods for DRA to obtain a better understanding of the existing methods. Further investigation and experimentation into therapeutic exercises is strongly recommended to identify the best set of exercises for a faster resolution. Further studies regarding the role of technology to assess therapeutic exercises would be worthwhile.

Outbreak of Pichia kudriavzevii fungaemia in a neonatal intensive care unit.

PURPOSE: Fungaemia is associated with substantial morbidity and mortality in neonates admitted to neonatal intensive care units (NICUs). We report an outbreak of fungaemia in a NICU due to rare yeast, Pichia kudriavzevii (a teleomorph of Candida krusei). To the best of our knowledge, this is the first report of neonatal sepsis due to P. kudriavzevii. METHODOLOGY: Between August and September 2014, blood cultures from nine neonates diagnosed with late-onset sepsis in the NICU yielded yeast-like organisms. The molecular identification and typing of these isolates was performed by sequencing the D1/D2 region of 26S rDNA and fluorescent amplified fragment length polymorphism (FAFLP) respectively. Antifungal susceptibility was tested by broth microdilution as per the Clinical Laboratory Standards Institute (CLSI) guidelines. Sampling from environmental sources and the hands of healthcare workers (HCWs) in the NICU was performed. RESULTS: Of the nine neonates, eight were preterm and six had very low birth weight (VLBW). Thrombocytopenia was present in two neonates. Sequencing identified all the isolates as P. kudriavzevii and FAFLP showed their clonal origin. Antifungal susceptibility testing revealed the susceptibility of all isolates to the antifungals tested. Treatment with voriconazole was advised. However, only seven neonates were treated successfully and discharged after improvement, whereas two were lost for follow-up. Cultures from the environment and the hands of HCWs were negative. The outbreak was controlled by the strict implementation of infection control practices. CONCLUSION: This study emphasizes the importance of accurate identification of the aetiological agent of sepsis and vigilant monitoring for the possibility of an outbreak in NICUs.