Title
AI-Based Blood Cancer Detection Using Deep Learning Techniques
Abstract
This project aims to develop an AI-powered model for the detection and classification of
blood cancers, particularly focusing on leukemia. Utilizing deep learning algorithms,
specifically convolutional neural networks (CNNs), the model will analyze peripheral
blood smear images to identify malignant cells. Preliminary studies indicate that such
models can achieve high accuracy rates, making them a promising tool for early
diagnosis. The proposed system will automate the detection process, reducing reliance
on manual examination by pathologists and potentially increasing diagnostic efficiency.
This approach not only aims to enhance accuracy but also seeks to provide a scalable
solution that can be integrated into clinical workflows.
Objective
The primary objectives of this project are:
● To design and implement a deep learning model that accurately detects and
classifies different types of blood cancers from medical images.
● To improve diagnostic accuracy and reduce the time required for analysis
compared to traditional methods.
● To create a user-friendly interface that allows healthcare professionals to easily
upload images and receive diagnostic results.
● To evaluate the model's performance using various metrics such as accuracy,
precision, recall, and F1-score.
System Specification
● Hardware Requirements:
● GPU-enabled workstation for training deep learning models (e.g., NVIDIA
GeForce RTX series).
● Minimum 16 GB RAM.
● Sufficient storage for datasets (SSD recommended).
● Software Requirements:
● Operating System: Windows/Linux.
● Programming Language: Python.
● Libraries: TensorFlow, Keras, OpenCV, NumPy, Matplotlib.
● Development Environment: Jupyter Notebook or Google Colab.
System Architecture
The system architecture consists of the following components:
1. Data Acquisition: Collection of peripheral blood smear images from medical
databases.
2. Preprocessing: Image enhancement techniques such as normalization, resizing,
and noise reduction to prepare data for analysis.
3. Model Training: Implementation of a CNN architecture for feature extraction and
classification. The model will be trained on labeled datasets containing both
healthy and cancerous cells.
4. Prediction: The trained model will be used to analyze new images and classify
them as either benign or malignant.
5. User Interface: A web-based application allowing users to upload images and
receive diagnostic results.
