Deep Learning Algorithms for Early Diagnosis of Acute Lymphoblastic Leukemia
Acute lymphoblastic leukemia (ALL) is a form of blood cancer that affects the white blood cells. ALL constitutes approximately 25% of pediatric cancers. Early diagnosis and treatment of ALL are crucial for improving patient outcomes. The task of identifying immature leukemic blasts from normal cells under the microscope can prove challenging, since the images of a healthy and cancerous cell appear similar morphologically. In this study, we propose a binary image classification model to assist in the diagnostic process of ALL. Our model takes as input microscopic images of blood samples and outputs a binary prediction of whether the sample is normal or cancerous. Our dataset consists of 10661 images out of 118 subjects. Deep learning techniques on convolutional neural network architectures were used to achieve accurate classification results. Our proposed method achieved 94.3% accuracy and could be used as an assisting tool for hematologists trying to predict the likelihood of a patient developing ALL.
Code (0)
등록된 구현이 없습니다.
Tasks
AllDeep LearningDiagnosticimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models
Leukemia, a severe form of blood cancer, claims thousands of lives each year. This study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using advanced image processing and deep learning techniques. By lev…
AllDeep LearningAcute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models
Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the question of their applicability and reliability remains unresolved. This stu…
AllAutomated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks
An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblast…
AllDetection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy bloo…
Transfer LearningQuaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis
The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies rev…