Microscopic image analysis plays a significant role in initial leukemia screening and its
efficient diagnostics. Since the present conventional methodologies partly rely on manual
examination, which is time consuming and depends greatly on the experience of domain
experts, automated leukemia detection opens up new possibilities to minimize human
intervention and provide more accurate clinical information. This paper proposes a novel
approach based on conventional digital image processing techniques and machine learning
algorithms and deep learning algorithms to automatically identify acute lymphoblastic
leukemia from peripheral blood smear images. 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. To overcome the greatest challenges in the segmentation phase, we implemented
extensive pre-processing and introduced a three phase filtration algorithm to achieve the best
segmentation results.