IEEE BASE PAPER TITLE:
Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique
OUR PROPOSED PROJECT TITLE:
Blood Cancer Detection and Classification using Deep Learning
IEEE BASE PAPER ABSTRACT:
Blood cancer-related illnesses may be challenging to examine and diagnose, both of which can take a significant period of time. During the course of the preceding decade, a variety of methods for detecting, analysing, and categorizing blood cancer in people were established. These methods include: However, as of right now, there is neither a method nor a model available that can automate the process of inspecting human blood cells to determine whether or not cancer is present. A model with these characteristics has the potential to improve disease detection and prevention, which would lead to a prompter medical diagnosis. This study presents the research progress made in the area of Deep Learning Model to identify the abnormalities in blood cells. In order to detect blood cancer, this study has used Hybrid Ensemble Deep Learning method. This method results with an accuracy of more than 95%.
PROJECT OUTPUT VIDEO:
OUR PROPOSED PROJECT ABSTRACT:
Blood cancer, also known as leukemia, is a life-threatening disease that requires early and accurate diagnosis for effective treatment. In this project, we present an innovative approach for the detection and classification of blood cancer using deep learning techniques. Specifically, we employ the MobileNetV2 architecture implemented in Python to achieve remarkable results in terms of accuracy.
The dataset utilized for this research is the “Blood Cells Cancer (ALL) dataset,” comprising four distinct classes: Benign, [Malignant] early Pre-B, [Malignant] Pre-B, and [Malignant] Pro-B. This dataset contains a total of 3242 peripheral blood smear (PBS) images. Accurate diagnosis of Acute Lymphoblastic Leukemia (ALL) through PBS images is a critical step in the early screening of cancer cases.
Our deep learning model, based on the MobileNetV2 architecture, has demonstrated exceptional performance. During training, it achieved an impressive accuracy of 98.00%, showcasing its ability to effectively distinguish between different types of blood cancer cells. Furthermore, the validation accuracy of 96.00% emphasizes the robustness and generalization capability of our model.
This project not only highlights the potential of deep learning in the field of medical image analysis but also contributes to the early detection and classification of blood cancer, ultimately improving patient outcomes. The utilization of MobileNetV2 and Python makes our solution both efficient and accessible for healthcare professionals, paving the way for enhanced cancer screening and diagnosis.
ALGORITHM / MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : FLASK.
Jayachitra; N. Umarkathaf, “Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique”, 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), IEEE Conference, 2023.