MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization.

An empirical study of object detection models for the detection of iron deficiency anemia using peripheral blood smear images.

Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1].

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine.

Identifying and diagnosing faults is a critical task in process industries to maintain effective monitoring of process and plant safety. Minimizing process downtime is critical for enhancing the quality of the product and minimizing production costs. Real-time categorization of issues across several levels is essential for the monitoring of processes.

Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.

The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ambiguity in their in vitro behavior. Intra-clonal population heterogeneity has also been identified and pre-clinical mechanistic studies suggest that these cumulatively depreciate the therapeutic effects of hMSC transplantation.

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar.

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake.

Floods are the most common natural disaster in several countries throughout the world. Flooding has a major impact on people's lives and livelihoods. The impact of flood disasters on human lives can be mitigated by developing effective flood forecasting and prediction models.

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.

Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background.

Design of Wearable Electrolarynx with Automatic Control.

Objective: The current work aims to design and develop an automatically controlled wearable electrolarynx, a voice substitution device for laryngeal carcinoma survivals.

Methods: The physical activity of mouth opening is sensed, amplified, and made to act as an enable signal to trigger the wearable electrolarynx. The resulting speech is recorded and compared for its voice reaction durations with that of manual electrolarynx and normal speaking methods.

Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

Background: Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease.

Investigating feasibility of 2021 WHO protocol for cervical cancer screening in underscreened populations: PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC).

Background: High-risk human papillomavirus (hrHPV) testing has been recommended by the World Health Organization as the primary screening test in cervical screening programs. The option of self-sampling for this screening method can potentially increase women's participation. Designing screening programs to implement this method among underscreened populations will require contextualized evidence.

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review.

Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al.

Improving symptom assessment and management in the community through capacity building of primary palliative care: A study protocol of exploratory research.

To determine the effectiveness of capacity building program on palliative care (PC) in enhancing the capacity of the primary health care workers in need assessment and symptom management of cancer patients. In India, less than one percent of people living with cancer have access to palliative care since most are from low- and middle-income groups. Accredited social health activist (ASHA) and primary health care workers are grassroots workers who are the first contact with family members and are seldom aware of PC in India.

Identification of Contractions from Electrohysterography for Prediction of Prolonged Labor.

The analysis of the uterine electrical activity and its propagation patterns could potentially predict the risk of prolonged/arrested progress of labor. In our study, the Electrohysterography (EHG) signals of 83 participants in labor at around 3-4 cm of cervical dilatation, were recorded for about 30 minutes each. These signals were analyzed for predicting prolonged labor.

AI-based carcinoma detection and classification using histopathological images: A systematic review.

Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides.

Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.

Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour.

BCHisto-Net: Breast histopathological image classification by global and local feature aggregation.

Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to detect the tumor. However, breast histopathological image analysis is inevitable for the detection of malignancy of the tumor.

Multi-channel Chan-Vese model for unsupervised segmentation of nuclei from breast histopathological images.

T he pathologist determines the malignancy of a breast tumor by studying the histopathological images. In particular, the characteristics and distribution of nuclei contribute greatly to the decision process. Hence, the segmentation of nuclei constitutes a crucial task in the classification of breast histopathological images.

Efficacy of choline and DHA supplements or enriched environment exposure during early adult obesity in mitigating its adverse impact through aging in rats.

Introduction: The aim of this study was to assess the efficacy of choline and DHA or exposure to environmental enrichment in obese adult and aging rats on alterations in body mass index, serum lipid profile and arterial wall changes, despite stopping high fat diet consumption and interventions during adulthood.

Methods: 21 day old male Sprague Dawley rats were assigned as Experiment-1 & 2 - PND rats were divided into 4 groups with interventions for 7 months (n = 8/group). NC- Normal control fed normal chow diet; OB- Obese group, fed high fat diet; OB + CHO + DHA- fed high fat diet and oral supplementation of choline, DHA.

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5, 10, 15 & 20, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain.

Photobiomodulation invigorating collagen deposition, proliferating cell nuclear antigen and Ki67 expression during dermal wound repair in mice.

The present investigation focuses on understanding the role of photobiomodulation in enhancing tissue proliferation. Circular excision wounds of diameter 1.5 cm were created on Swiss albino mice and treated immediately with 2 J/cm and 10 J/cm single exposures of the Helium-Neon laser along with sham-irradiated controls.

Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.

Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs.

Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images.

Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia.