<p>About 300,000 leukemia cases are diagnosed every year, with the total number of active cases rising to 2.3 million in 2015. Although the number of adults diagnosed with leukemia is pretty high, this is the most common type of cancer found in children in developed countries. Its ability to recur and expensive diagnostic process make patients unable to undergo the diagnosis on a timely basis and consequently can prove fatal for many. The proposed novel model PyraNet aims to tackle the requirement of high-precision machinery and human expertise, as there might not be enough resources for the latter. Proportionate fine-tuning and construction make the model to accurately and precisely detect the presence of leukemic blast cells and classify them into their respective class types. Also, this novel architecture proposed here is a step towards solving the problem of unbalanced classes that often arises when the quantitative distribution of data within different classes is highly biased. As an initiative to tackle it, we have used multi-model-layer training. The analysis of experimental results shows that the proposed model is capable of correctly predicting a higher number of classes with better accuracy.</p>