Deep Learning Projects (original) (raw)

Last Updated : 15 Apr, 2026

Deep learning projects involve building intelligent systems using neural networks to solve complex problems such as image recognition, natural language processing and speech analysis. In this article, we are going to explain different Deep Learning Projects.

Medical Diagnoser

A model that predicts diseases based on patient symptoms using a feedforward neural network.

• Uses symptom–disease dataset (CSV format)
• Feedforward neural network with input, hidden, output layers
• Adam optimizer + binary cross-entropy loss
• Includes preprocessing (missing values, normalization)
• Predicts disease probabilities for new patients

Healthcare Chatbot

A chatbot that answers medical queries using deep learning and NLP techniques.

• Uses healthcare Q&A dataset
• Text preprocessing and vocabulary creation
• LSTM model for intent recognition
• Generates responses based on user input
• Can be deployed as a web-based chatbot

Hate Speech Detection

A text classification model that detects hate speech in online content.

• Classifies text as hate or non-hate
• Uses NLP preprocessing techniques
• Models: RNN, LSTM, BERT
• Captures contextual meaning in text
• Helps in content moderation systems

Lung Cancer Detection

A CNN-based system to detect lung cancer from CT scan images.

• Uses CT scan image dataset
• Image preprocessing (resize, normalize, augment)
• CNN with convolution + pooling layers
• Trained and evaluated on medical images
• Helps in early cancer detection

Age Detection

A model that predicts age from facial images using computer vision.

• Uses OpenCV for face detection
• Applies transfer learning (VGG16/ResNet)
• Preprocessing: alignment and normalization
• Uses dropout to reduce overfitting
• Predicts age from images

Image colorization

A model that converts black-and-white images into colored images.

• Works on grayscale image datasets
• Uses CNN-based architecture
• Image preprocessing with OpenCV
• Learns patterns to assign colors
• Useful for restoring old photos

Pneumonia Detection

A CNN-based system to detect pneumonia from chest X-rays.

• Uses labeled chest X-ray dataset
• Image preprocessing and augmentation
• CNN for image classification
• Trained on medical imaging data
• Assists in faster diagnosis

Holistically-Nested Edge Detection

A deep learning approach for accurate edge detection in images.

• Uses CNN for edge detection
• Detects edges at multiple scales
• Better than traditional methods
• Works well on complex images
• Implemented using OpenCV

IPL Score Prediction

A model that predicts IPL match scores using historical data.

• Uses match and player statistics
• Data preprocessing and feature engineering
• Models: ANN, RNN, LSTM
• Regression-based prediction
• Useful for analysis and forecasting

Image Caption Generator

A model that generates captions for images using deep learning.

• Uses Flickr8K image dataset
• Combines CNN + LSTM models
• Image and text preprocessing
• Tokenization and vocabulary mapping
• Generates descriptive captions

Human Activity Recognition

A system that detects human activities using sensor data.

• Uses data from sensors (accelerometer, gyroscope)
• Preprocessing and feature extraction
• Models: CNN, RNN, LSTM
• Classifies activities like walking, running
• Used in fitness and healthcare

Image Caption Generation

A project that analyzes character relationships and sentiments.

• Text preprocessing (cleaning, tokenization)
• Builds character relationship networks
• Uses graph-based visualization
• Applies community detection algorithms
• Extracts insights from movie data

Prediction of Wine type

A classification model that predicts wine types based on features.

• Uses chemical and sensory data
• Data preprocessing and normalization
• Deep learning classification model
• Learns patterns in wine properties
• Useful for wine categorization

Flight Delay Prediction

A model that predicts flight delays using historical flight data.

• Uses features like time, route, weather
• Data cleaning and feature engineering
• Models: RNN, LSTM, TCN
• Handles time-series data
• Uses ensemble methods for accuracy