Diagnosis of Leukemia and its types Using Digital Image Processing Techniques

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

“…To address the challenge of manually detecting blasted cells, Dasariraju et al [ 54 ], Inbarani et al [ 66 ], Abedy et al [ 29 ], Jagadev and Virani [ 34 ], and Dharani and Hariprasath [ 31 ] used medical images of healthy and malignant samples to automatically identify the leukemic types and subtypes. While Dasariraju et al [ 54 ] applied an RF algorithm as an approach to differentiate between abnormal and healthy leukocytes, and classify immature leukocytes into their 4 subtypes, Inbarani et al [ 66 ] discussed the implementation of a novel sophisticated approach to identify ALL blast cells via the histogram-based soft covering rough K-means clustering (HSCRKM) segmentation algorithm.…”

“…Alongside the usage of imaging techniques, many studies employed other techniques; for example, Moraes et al [ 133 ] suggested the usage of flow cytometry data for distinguishing leukemia/lymphoma, and Mahmood et al [ 143 ] directed their research to focus more on identifying the most discriminatory features for CLL using patient laboratory test results, demographic parameters, and training a Classification and Regression Trees model on 94 pediatric patients, which was evaluated using 10-fold cross validation. Moreover, both Dharani and Hariprasath [ 31 ] and Jagadev and Virani [ 34 ] used SVM to classify leukemia and its subtypes, while Paswan and Rathore [ 28 ] used K-nearest neighbors to separate blasted blood cells from normal ones and classify them further into either AML or ALL using a value of K=4. By contrast, Moraes et al [ 133 ] suggested the implementation of decision tree as an ML-based technique for distinguishing leukemia/lymphoma, where a binary classification between healthy and immature leukocytes was performed with an 80%/20% data split, followed by a subclassification of immature leukocytes into their respective 4 types using a 70%/30% split, and several combinations of hyperparameters were evaluated during a 5-fold cross validation.…”

Image Processing Techniques for Acute Leukaemia Detection

“…[14] proves that selecting only a green component from an RGB image can also result in better accuracy in segmentation. [11,12,15,17,18,19] have used histogram equalization to improve image contrast. Some of the articles have used various filtering techniques such as Order statistic filter [18], wiener filtering [15,10], Selective filtering [7] etc.…”

Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

“…Furthermore, the second pre-processing achieved higher accuracy at 0.8545 International Journal of Intelligent Engineering and Systems, Vol. 14 and precision at 0.8418. Moreover, the third preprocessing obtained precision at 0.8361 and accuracy at 0.8543.…”

“…Additionally, [14] utilized an image processing approach to analyzed healthy and infected leukemia blood smears, [15] developed a system to identify and classify various types of leukemia based on image processing techniques. This research achieved an accuracy of 80% for the ALL detection separately of one cell, 100% for AML detection separately of one cell, as well as 90% for cell detection throughout many cells, [16] proposed a leukemia prediction method using the histogram of oriented gradients (HOG) feature descriptor and logistics regression.…”