ABSTRACT
- Blood cancer is a life-threatening disease that disrupts the normal production and function of blood cells, significantly impacting human health. Early and accurate detection is essential for timely medical intervention, improved treatment planning, and increased survival rates. Traditional diagnostic methods, such as microscopic analysis of blood smears, are often time-consuming, prone to human error, and require expert interpretation. To address these challenges, this project introduces a deep learning-based approach for blood cancer detection using the MobileNet algorithm, a lightweight yet powerful convolutional neural network (CNN) architecture.
- The proposed system utilizes a dataset of microscopic blood smear images, where MobileNet is trained to classify samples into cancerous and non-cancerous categories. The methodology involves a series of preprocessing steps, including image enhancement, noise reduction, and normalization, to optimize feature extraction and improve classification accuracy. MobileNet’s depthwise separable convolutions allow for efficient computation, making it well-suited for real-time applications with limited hardware resources.
- Experimental results demonstrate that the MobileNet-based model achieves high classification accuracy while ensuring fast inference, making it a viable solution for automated blood cancer screening. The lightweight architecture of MobileNet enables deployment on edge devices and cloud-based diagnostic platforms, facilitating accessibility in resource-constrained environments. This research highlights the potential of deep learning in revolutionizing medical diagnostics by offering a scalable, cost-effective, and highly accurate approach for blood cancer detection.
EXISTING SYSTEM
- Microscopic Examination of Blood Smears
- Pathologists analyze blood smear images under a microscope to identify abnormal cell structures.
- This process is time-consuming and requires specialized expertise.
- Automated Systems:
- Use traditional machine learning techniques or complex deep learning models based on CNN.
- High computational requirements limit deployment on mobile devices.
DISADVANTAGES
- Subjectivity: Human error in visual diagnosis.
- High Costs: Biopsies and advanced diagnostic tools can be expensive.
- Expert Dependency: Requires highly skilled pathologists, making early detection difficult in resource-limited settings.
- Computational Complexity: Many deep learning models require significant computational power, making real-time web deployment challenging.
PROPOSED SYSTEM
- The proposed system aims to enhance the accuracy and efficiency of blood cancer detection using the MobileNet algorithm, a lightweight convolutional neural network (CNN) optimized for fast and efficient image classification.
- Unlike traditional CNN models, which are computationally intensive, MobileNet employs depthwise separable convolutions to reduce the number of parameters, making it an ideal choice for medical image analysis with limited computational resources.
- In this system, blood smear images are collected and preprocessed using noise reduction, contrast enhancement, and normalization techniques. The images are then resized to match the input dimensions required by MobileNet (e.g., 224×224 pixels).
- The preprocessed images are fed into the MobileNet model, which extracts essential features such as cell shape, texture, and structural abnormalities.
- The extracted features are passed through fully connected layers, followed by a softmax activation function that classifies the images as cancerous or non-cancerous.
- To improve performance, the proposed system integrates transfer learning, where a pre-trained MobileNet model is fine-tuned using a domain-specific dataset.
- This approach enhances the system’s ability to generalize across different types of blood cancer.
- Additionally, techniques like data augmentation and class balancing are implemented to address dataset imbalance issues and prevent model bias.
- The model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliability.
ADVANTAGES
- High Accuracy and Efficiency – The MobileNet-based deep learning model ensures accurate classification of blood smear images into cancerous and non-cancerous categories, reducing the chances of misdiagnosis.
- Lightweight and Fast Inference – MobileNet’s depthwise separable convolutions significantly reduce computational complexity, allowing faster processing and real-time diagnosis.
- Automated and Objective Diagnosis – Unlike traditional manual microscopic analysis, which is prone to human error, the proposed system provides consistent, unbiased, and automated detection of blood cancer.
- Effective Image Preprocessing – Advanced preprocessing techniques such as image enhancement, noise reduction, and normalization improve feature extraction, leading to better model performance.
- Scalability and Deployment Flexibility – The lightweight architecture of MobileNet makes the system suitable for deployment on mobile devices, cloud platforms, and edge computing environments, ensuring accessibility in remote or resource-limited areas.
- Reduced Dependence on Medical Experts – The automated detection system minimizes the need for continuous expert supervision, allowing hospitals and clinics to optimize their workflow and focus on critical cases.
- Cost-Effective Solution – By integrating deep learning with automated analysis, the system reduces the costs associated with manual microscopic examinations and extensive laboratory procedures.
PROJECT DEMO VIDEO
HARDWARE REQUIREMENTS:
- System : Intel i3 Processor Mimimum.
- Hard Disk : 20 GB Space
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
SOFTWARE REQUIREMENTS:
- •Operating system : Windows 10 Pro. /Mac Os
- •Coding Language : Python, HTML, CSS,JS
- •Web Framework : Flask
Including Packages
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- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares