• S V Rajesh Kumar
• G Umesh Chandra
• S Suryateja
• K Vamsi
• K Udhaya Bhanu

Leukemia, a critical form of blood cancer, demands precise and timely diagnosis for effective treatment. In this study, we present a Graphical User Interface (GUI)-based approach for the detection of leukemia disease utilizing a Deep Convolutional Neural Network (DCNN). Our method integrates various image processing and machine learning techniques, including input image preprocessing, filter application, binarization, hybrid contour extraction, and cell masking, followed by classification using a DCNN Classifier. We conducted comprehensive analysis and evaluation, achieving an impressive accuracy of 93.62% and sensitivity of 94.51% on a diverse dataset of microscopic blood cell images. To benchmark our approach, we compared it with an existing method employing Multiclass Support Vector Machine (MSVM), which attained an accuracy of 92% and sensitivity of 93.71%. Our results demonstrate the superiority of the proposed DCNN-based approach in terms of accuracy and sensitivity, highlighting its potential as an advanced diagnostic tool for leukemia detection. The graphical interface enhances accessibility and usability, making it suitable for clinical deployment and contributing to the advancement of automated leukemia diagnosis.

DCNN, Luckemia, SVM, Accuracy, Sensitivity.

"GUI Based Detection of Leukemia Disease Using Deep Convolution Neural Network ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g487-g493, April-2024, Available :http://www.jetir.org/papers/JETIR2404665.pdf

"GUI Based Detection of Leukemia Disease Using Deep Convolution Neural Network ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg487-g493, April-2024, Available at : http://www.jetir.org/papers/JETIR2404665.pdf