Paper Title
Proposing an Efficient CNN Model for Detection of Acute Lymphoblastic Leukemia (ALL) using Transfer Learning

Abstract - In the case of acute lymphoblastic leukemia (ALL), the growth of vast numbers of immature lymphocytes characterizes this malignancy. A pathologist's study of the bone marrow confirms the leukemia kinds. Leukemia cell types may be identified using a time-consuming and error-prone conventional approach, which relies on the expertise of the specialist. Due to the timing and accuracy requirements, an automatic approach is required. Many machine learning and deep learning algorithms may be used to estimate the leukemia cells present in a picture. DenseNet 169, VGG19, and a basic CNN model were used to train three convolutional neural networks (CNNs) on the IDB-1 dataset of all pictures; we achieved an accuracy rate of 98.93 percent, 99.49 percent, and 99.30 percent correspondingly. Keywords - Acute Lymphoblastic Leukemia, DenseNet 169, VGG19, Simple CNN Model, IDB-1 Dataset.