Cancer has been plaguing the society for a long time and still there is no certain treatment; especially if detected in later stages. That is why early detection and treatment of cancer is of utmost importance. Acute lymphoblastic leukemia is a type of blood cancer which is known to progress very rapidly and prove fatal if there is a delay in detection. Detection of this type of cancer is carried out manually by observing the blood samples of patient under microscope and conducting various other tests. This process may produce undesirable drawbacks: slowness, nonstandardized accuracy since it depends on examiner's / pathologist's capabilities and fatigue due to work overload can cause human errors in detection. A few automated systems for detection of Acute Lymphoblastic Leukemia (ALL) have been proposed which involve extracting features from blood images using MATLAB and implementing different classifiers to produce results, which gave remarkable accuracies though not enough for practical usage. Our proposed system is further improving the classification accuracy. It uses openCV and skimage for image processing to extract relevant features from blood image and not just sheer number of features and further classification is carried out using various classifiers: CNN, FNN, SVM and KNN of which CNN gives the highest accuracy of 98.33%. CNN and FNN are written using TensorFlow framework. The accuracies obtained by other classifiers: FNN, SVM, and KNN are 95.40%, 91.40% and 93.30% respectively. © 2018 IEEE.