• SANJAY R
• Sam Solomon.J
• Rohitbalaji.b

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that primarily affects children. Early and accurate diagnosis of ALL is crucial for effective treatment and improved patient outcomes. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in medical image analysis tasks, including the detection of leukemia from blood smear images. This paper presents the implementation of a customized CNN model for the detection of ALL from blood smear images. The proposed model leverages the power of deep learning to automatically classify leukemia and normal cells with high accuracy. The model’s performance is evaluated using various metrics, including accuracy, precision, recall, and F1-score, on a pre-trained dataset obtained from Kaggle. The results demonstrate the effectiveness of the CNN model in accurately identifying leukemia cells, highlighting its potential as a valuable tool for assisting pathologists and hematologists in diagnosing ALL. The implementation of deep learning-based models for leukemia detection holds promise for improving diagnostic accuracy, expediting treatment initiation, and ultimately, enhancing patient care in clinical practice.

Acute Lymphoblastic Leukemia (ALL) Blood cancer Children Early diagnosis Accurate diagnosis Deep learning Convolutional Neural Networks (CNNs) Medical image analysis Leukemia detection Blood smear images Customized CNN model Classification Leukemia cells Normal cells Accuracy Precision Recall F1-score Pre-trained dataset Kaggle Pathologists Hematologists Diagnostic accuracy Treatment initiation Patient care Clinical practice

"ACUTE LYMPHOBLASTIC LEUKEMIA DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.g739-g743, March-2024, Available :http://www.jetir.org/papers/JETIR2403700.pdf

"ACUTE LYMPHOBLASTIC LEUKEMIA DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppg739-g743, March-2024, Available at : http://www.jetir.org/papers/JETIR2403700.pdf