Acute lymphoblastic leukemia detection approach from peripheral blood smear using color threshold and morphological techniques

Acute lymphoblastic leukemia (ALL) has recently been one of the most significant concerns in cancers, especially child and old age. Therefore, crying needs to diagnose leukemia as early as possible, increasing the treatment options and patient survivability. Some basic handicraft leukemia detection processes have been introduced in this arena though these are not so accurate and efficient. The proposed approach has been introduced an automated ALL recognition system from the peripheral blood smear. Initially, the color threshold has been applied to segment lymphocytes blood cells from the blood smear. Some post-processing techniques like morphological operation and watershed have been executed to segment the particular lymphocytes cell. Finally, we used a support vector machine (SVM) classifier to classify the cancerous image frames using a statistical feature vector obtained from the segmented image. The proposed framework has achieved the highest accuracy of 99.21%, the sensitivity of 98.45%, specificity of 99%, the precision of 99%, and F1 score of 99.1%, which has beat existing and common states of art methods. We are confident that the proposed approach will positively impact the ALL detection arena.