Detection of Leukemia Using Transfer Learning
Biplab Kanti Das, Chanchal Ghosh, Joydeb Sheet, Himadri Sekhar Dutta · 2024
Leukemia is a kind of cancer that targets White Blood Cells (WBCs) and influences equally children and adults, caused by the neoplastic proliferation of bone marrow precursor cells, resulting in early death and a variety of other side effects. As a result, various manual techniques for detecting leukemia have been developed, but their performance is not up to the mark as the results were not accurate and reliable. A bone marrow investigation may be advised to check and determine the kind of blood cancer or leukemia. This conventional method is time-taking, and the accuracy of detection depends upon the efficiency of the pathologists. Early detection by using computerized techniques can significantly enhance the rate of cure. Convolutional Neural Networks (CNNs) are more and more getting used in the classification and diagnosis of medical image processing. This article suggests a new automated classification technique using transfer learning to identify Leukemia based on microscopic blood images, which will overcome the drawbacks of traditional methods and provides more accuracy. Pre-processed blood smear images, move on to feature extraction. In the feature extraction phase, Inception V3, a pre-trained Deep Convolution Neural Network (DCNN) is used to extract features. To shorten training time and improve efficiency, a pre-trained model is applied along with small datasets into a new model. The Inception-V3 model serves as a base, where on top there is a Fully-Connected (FC) layer for optimizing the process of classification. Convolution layers can learn enough with their convolution kernel to produce tensor outputs during the model-building process. Studies were carried out on a dataset of 15135 images, affirming that the model outperforms existing methods due to its 97.34% accuracy in classification.