Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Abstract

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

High-Throughput, Label-Free Isolation of White Blood Cells from Whole Blood Using Parallel Spiral Microchannels with U-Shaped Cross-Section

“…In medical science, the study of characteristics and mechanisms of cell functions, identifying external damaging factors, and finding methods to prevent and treat cellular disorder diseases in the human body requires the preparation of cell samples with suitable purity. Blood is the most important component of the human body, containing various types of essential cells, including red blood cells (RBCs), white blood cells (WBCs), and platelets which are produced in the bone marrow and released into the bloodstream [ 1 ]. WBCs comprise about 1% of whole blood and have a significant role in the body’s immune system.…”

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

“…In the medical domain, automated disease classification plays an important role during the mass data assessment and a perfectly tuned disease classification system further reduces the diagnostic burden of physicians and acts as an assisting system during the decision-making process [ 32 , 33 , 34 , 35 ]. Therefore, a considerable number of disease detection systems assisted by DL are proposed and implemented in the literature [ 36 , 37 , 38 , 39 , 40 ]. Recent DL schemes implemented in the LIDC-IDRI with fused deep and HCF helped achieve a classification accuracy of >97% [ 13 ].…”

An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

“…An integrated system was proposed where visual descriptors were used to measure the distance between the people and the methodology was able to achieve an accuracy of 93.3% [37]. Another deep learning solution was proposed that alerts the person as soon as they violate the social distancing norms via CCTV and PoseNet [38,39]. The research concluded that using a hybrid deep transfer learning model based on ResNet-50 and machine learning algorithms such as SVM, decision trees, etc.…”