MediCheck: A Design Thinking Approach to Early-Stage Childhood Cancer Detection and Geolocation-Enabled Diagnostic Support in India

Fri, 26 Jun 2026 00:00:00 +0000

Catching childhood cancer early is the single most critical factor in determining survival. In India, where roughly 75,000 children are diagnosed every year, survival rates remain low, mostly because of delays in finding care, rural-urban disparities, and a lack of baseline awareness. In this study, they share the design and implementation of MediCheck, a web-based clinical screening and hospital mapping application built to guide parents from initial symptom concern to professional oncology consultations. Following a five-stage Design Thinking process (Empathize, Define, Ideate, Prototype, and Test), they structured the tool around a weighted symptom overlap index, real-time explainability features, and side-by-side benign differential diagnosis cards to ease parent anxiety. Geolocation-aware mapping connects users directly with oncology units via the Google Places API. To check the system's underlying logic, they ran two validation pipelines: a clinical risk classifier trained on synthetic symptom records (achieving 98.9% accuracy with an RF-Gradient Boosting ensemble) and a cell smear classifier trained on augmented blood images (reaching 95% accuracy in flagging lymphoblastic leukemia). User feedback showed that the platform helps parents organize symptom timelines, making consultations with doctors more effective.

AquaClean AI: Intelligent Underwater Trash Detection using Hybrid CNN and YOLO-based Deep Learning for Marine Pollution Monitoring

Premala Bhande, Divya — Thu, 25 Jun 2026 00:00:00 +0000

Marine pollution is emerging as a critical global issue that threatens aquatic ecosystems and biodiversity at multiple levels. The accumulation of underwater waste disrupts ecological balance and demands intelligent monitoring solutions. Within the paradigm of deep learning-driven environmental systems, AquaClean AI is conceptualized as an intelligent framework that enables automated detection of underwater trash. The system integrates hybrid convolutional neural networks combining VGG16 and EfficientNet to extract deep visual representations from complex underwater imagery. It further employs YOLO-based object detection to localize and classify debris under challenging environmental conditions. The framework operationalizes computer vision principles to process distorted images affected by low visibility and noise while ensuring reliable detection performance. By synthesizing feature extraction and real-time detection into a unified pipeline, the system transforms raw underwater data into meaningful insights. The proposed approach demonstrates strong adaptability across diverse underwater scenarios and supports continuous monitoring applications. Experimental evaluation demonstrates high detection accuracy, with YOLO-based models achieving mAP of 0.96, precision of 0.94-0.96, and recall of 0.91-0.93, outperforming traditional detection approaches. The solution offers a scalable, intelligent approach to addressing underwater pollution using advanced artificial intelligence techniques.

Journal of Computer Based Parallel Programming

MediCheck: A Design Thinking Approach to Early-Stage Childhood Cancer Detection and Geolocation-Enabled Diagnostic Support in India

AquaClean AI: Intelligent Underwater Trash Detection using Hybrid CNN and YOLO-based Deep Learning for Marine Pollution Monitoring