It is reported that since 2016 there are over sixty thousand diagnosed cases of Leukemia in the United States of America alone. It also suggests that Leukemia is the most common type of cancer seen in the age of twenty. Although the study is based on a Western country, it is equally alarming for an Asian country like Bangladesh where healthcare system is not up to the standard. Researches show that the Chronic Lymphocytic Leukemia has about 83% five-year long survival rates. This paper focuses on Acute Lymphocytic Leukemia (ALL) as this is the most common type of Leukemia in Bangladesh. It is common knowledge among oncologists, that cancer is much easier to treat if it is detected in the early stages. Thus the treatment needs to begin as early as possible. We propose a hands-on approach in detecting the irregular blood components (e.g., Neutrophils, Eosinophils, Basophils, Lymphocytes and Monocytes) that are typically found in a cancer patient. In this work, we first identify 14 attributes to prepare the dataset and determine 4 major attributes that play a significant role in determining a Leukemia patient. We have also collected 256 primary data from Leukemia patient. The data is then processed using microscope to obtain images and fetch into Faster-RCNN machine learning algorithm to predict the odds of cancer cells forming. Here we have applied two loss functions to both the RPN (Region Convolutional Neural Network) model and the classifier model to detect the similar blood object. After identifying the object, we have calculated the corresponding object and based on the count of the corresponding object we finally detect Leukemia. The mean average precision observed are 0.10, 0.16 and 0, where the epochs are 40, 60 and 120, respectively.