IJAIEM

Billa Rohan Reddy , S.Revathi , Thota Sai Kiran ,A.Bhasha

Abstract

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that affects white blood cells, primarily affecting children. Early diagnosis is crucial for successful treatment and recovery. In recent years, convolutional neural networks (CNNs) have been increasingly used in medical image analysis, including the detection and classification of ALL from medical images. In this paper, we propose a model called QCResNet for the classification of ALL from peripheral blood smear images. Our proposed model achieved a high accuracy of 98.9% on a dataset of 15,135 images, outperforming several state-of-the-art methods. Our results demonstrate the potential of QCResNet for accurate and rapid effective diagnosis of acute lymphocytes. The model incorporates multiple convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification.