AIMC Topic: Deep Learning

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Deep-learning approach to detect childhood glaucoma based on periocular photograph.

Scientific reports
Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on ...

An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology.

Scientific reports
Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper intro...

Drug synergy model for malignant diseases using deep learning.

Journal of bioinformatics and computational biology
Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when compared to single-drug doses. Thus, it has attained significant interest from acad...

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

Computers in biology and medicine
The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which...

Multi-modal deep learning for automated assembly of periapical radiographs.

Journal of dentistry
OBJECTIVES: Periapical radiographs are oftentimes taken in series to display all teeth present in the oral cavity. Our aim was to automatically assemble such a series of periapical radiographs into an anatomically correct status using a multi-modal d...

Automatic retinoblastoma screening and surveillance using deep learning.

British journal of cancer
BACKGROUND: Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the globe salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term sur...

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-c...

A systematic approach to deep learning-based nodule detection in chest radiographs.

Scientific reports
Lung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the detection a...

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations.

Computers in biology and medicine
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detect...

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

European radiology
OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI.