Deep Learning Pipeline for Cancer Detection

Project Overview: This project focuses on developing an automated deep learning pipeline to assist radiologists in detecting cancer from Computed Tomography (CT) scans. The system leverages advanced image processing and neural network models to identify potential tumors with high sensitivity.

Motivation

Cancer remains one of the leading causes of mortality worldwide. Early and accurate detection significantly improves patient survival rates. However, manual analysis of CT scans is time-consuming and prone to human error. An AI-powered pipeline can provide a reliable second opinion.

Pipeline Workflow

1. Data Preprocessing: Noise reduction, normalization, and image augmentation.
2. Segmentation: Isolating regions of interest (lungs, tissues, or tumors).
3. Feature Extraction: Using Convolutional Neural Networks (CNNs) to capture key patterns.
4. Classification: Applying deep neural networks to classify scans as benign or malignant.
5. Validation: Performance evaluation with metrics such as accuracy, precision, recall, and AUC-ROC.

Technologies Used

• Python, TensorFlow, PyTorch
• CT Imaging datasets (LIDC-IDRI, Kaggle datasets)
• GPU-based training infrastructure

Expected Outcomes

The pipeline is expected to provide:

• Early detection of cancerous lesions in CT scans.
• Reduced workload for radiologists through automation.
• Scalable deployment for hospital diagnostic systems.

“AI-assisted diagnostics could significantly reduce missed cancer cases and accelerate treatment planning.”

AI-based Radiology Assistant

Project Overview

The AI-based Radiology Assistant is designed to streamline the process of generating structured radiology reports using Natural Language Processing (NLP). By analyzing CT, MRI, and X-ray images, the system automatically extracts key findings and generates human-like, clinically accurate diagnostic summaries for radiologists.

Motivation

Radiology departments handle thousands of scans daily, and manual report generation is time-consuming and prone to inconsistencies. This assistant aims to reduce reporting time, enhance accuracy, and provide decision support to medical professionals.

System Workflow

1. Image Analysis: AI models detect anomalies and highlight key observations.
2. Feature Extraction: Medical imaging features are mapped to clinically relevant terms.
3. NLP Report Generation: A transformer-based NLP model generates structured, human-readable reports.
4. Validation: Radiologists review and finalize the reports before patient release.

Key Features

• Automated abnormality detection with high accuracy.
• Consistent and structured reporting using medical terminology standards (SNOMED, ICD).
• Multilingual support for global healthcare providers.
• Integration with existing PACS (Picture Archiving and Communication System).

Technologies Used

• Deep learning for medical imaging (CNNs, Vision Transformers).
• Transformer-based NLP models (BERT, GPT variants).
• DICOM processing tools.
• Cloud-based deployment with scalable APIs.

Expected Outcomes

• Faster turnaround time for radiology reporting.
• Reduced cognitive load for radiologists.
• Standardized, high-quality diagnostic documentation.
• Improved patient outcomes through timely diagnosis.

Smart Microgrids – Solar–Wind Hybrid Model with IoT Load Balancing

Project Overview

The Smart Microgrids project focuses on integrating solar and wind energy into a hybrid system that leverages IoT-enabled load balancing. By intelligently distributing power across residential, industrial, and commercial loads, the system aims to maximize renewable energy utilization while ensuring stability and efficiency.

Motivation

Traditional grids often struggle with instability and inefficiency due to fluctuating renewable energy sources. A smart microgrid allows communities to become energy independent, reduce carbon emissions, and optimize resource allocation. IoT devices enable real-time monitoring and dynamic load management, making hybrid energy more reliable.

System Workflow

1. Energy Harvesting: Solar panels and wind turbines generate electricity.
2. Storage: Excess energy is stored in advanced battery systems.
3. IoT Monitoring: Smart sensors track energy production and consumption patterns.
4. Load Balancing: AI-driven IoT controllers prioritize critical loads and distribute power accordingly.
5. Grid Interaction: The microgrid exchanges surplus or deficit power with the main grid when necessary.

Key Features

• Hybrid solar–wind energy generation for continuous supply.
• IoT-enabled smart meters and controllers for real-time data exchange.
• Adaptive load balancing to minimize energy wastage.
• Cloud-based dashboard for monitoring system health and performance.
• Support for demand response and peak shaving.

Technologies Used

• Renewable energy systems (solar PV, wind turbines).
• IoT sensors, smart meters, and edge computing devices.
• AI algorithms for predictive load management.
• Cloud integration with analytics and visualization tools.
• Battery storage and power electronics for energy stability.

Expected Outcomes

• Increased renewable energy penetration into local grids.
• Improved reliability of power supply in rural and urban communities.
• Reduced carbon footprint through optimized energy usage.
• Cost savings for consumers via efficient demand-side management.
• Scalable model for smart cities and sustainable development initiatives.