Detection of ALL Disease

Project Code :TCMAPY961


The objective of Detection of Acute Lymphoblastic Leukemia Disease is to develop a computer-aided diagnosis (CAD) system that can accurately detect the presence of acute lymphoblastic leukemia (ALL) from blood cell images. The system uses various image processing techniques and machine learning algorithms to identify abnormal cells and classify them as either leukemia or non-leukemia.


Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer-aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers CNN Experimental results reveal that the proposed method achieved accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.


NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram




Processor- I3/Intel Processor

Hard Disk- 160GB

Key Board- Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA



Operating System:  Windows 7/8/10

Server side Script:  HTML, CSS, Bootstrap & JS

Programming Language:  Python

Libraries:  Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

IDE/Workbench:  PyCharm

Technology:  Python 3.6+

Server Deployment:  Xampp Server

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