IARJSET ISSN (O) 2393-8021, ISSN (P) 2394-1588
International Advanced Research Journal in Science, Engineering and Technology
ISO 3297:2007 Certified Impact Factor 7.105 Vol. 9, Issue 6, June 2022
DOI: 10.17148/IARJSET.2022.9639
Blood Cancer Detection Using Machine Learning
Akash Holker1, Naveena Hegde2, Rakesh M P3 , Yallalinga4, Sushmitha M5
Student, Department of Information Science, Acharya Institute Of Technology, Bengaluru, India 1-4
Assistant Professor, Department of Information Science, Acharya Institute Of Technology, Bengaluru, India 5
Abstract: Leukemia (blood cancer) begins in the bone marrow and causes the formation of a large number of abnormal
cells. The most common types of leukemia known are Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia
(AML), Chronic lymphocytic leukemia (CLL) and Chronic myeloid leukemia (CML). This project makes an effort to
devise a methodology for the detection of Leukemia using image processing techniques, thus automating the detection
process. Our project consist of development of a machine learning algorithm to detect cancer using microscopy image.
Keywords: Machine Learning, Blood Cancer, Leukemia, Image Processing, CNN Architecture
I. INTRODUCTION
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous
cells are abnormal areas often growing in any part of human body that are life-threatening. Even though modality has
different considerations, such as complicated history, improper diagnostics and treatment that are main causes of deaths.
The aim of is to analyse, review, cancer detection using machine learning techniques for cancer leukemia. The study
highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep
learning techniques.
Figure 1 : Blood Sample
Right side sample consist of abnormally grown white blood cells. These distinct features can be used in detection of
leukemia by machine learning module.
Microscopic blood tests are considered as the main procedure for the diagnosis of leukemia. Analysis of blood smears is
the most common way of discovering leukemia, but not the only one technique. Interventional radiology is an alternative
technique for the diagnosis of leukemia. However, radiological techniques, such as percutaneous aspiration, biopsy, and
catheter drainage, suffer from inheriting limitations of imaging modality sensitivity and resolution of the radio images.
Moreover, other techniques, such as Molecular Cytogenetics, Long Distance Inverse Polymerase Chain Reaction (LDI-
PCR), and Array-based Comparative Genomic Hybridization (aCGH), need extensive work and time to identify leukemia
types. Due to time and cost requirements of these techniques, microscopic blood tests and bone marrow are the most
common methods for identification of leukemia subtypes.
II. LITERATURE REVIEW
• Raghav Kandhari, Anupama Bhan, Parth Bhatnagar shows Computer based diagnosis of Leukemia in blood
smear images Shows blood images using image processing.
• Patil Babaso S, S.K. Mishra, Aparna Junnarkar illustrates Leukemia Diagnosis Based on Machine Learning
Algorithms.
© IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 251
IARJSET ISSN (O) 2393-8021, ISSN (P) 2394-1588
International Advanced Research Journal in Science, Engineering and Technology
ISO 3297:2007 Certified Impact Factor 7.105 Vol. 9, Issue 6, June 2022
DOI: 10.17148/IARJSET.2022.9639
• N. H. M. Daud, R. A. A. Raof, M. K. Osman, N. H. Harun describes Segmentation Technique for Nucleus
Detection in Blood Images for Chronic Leukaemia.
• Dharani T, Hariprasath S will shows the Diagnosis of Leukemia and its types Using Digital Image Processing
Techniques.
• Atharva Bankar, Kewal Padamwar, and Aditi Jahagirdar calculates Symptom Analysis using a Machine
Learning approach for Early Stage Lung Cancer.
• Subhash Rajpurohit Sanket Patil Nitu Choudhary, Shreya Gavasane Prof. Pranali Kosamkar describes .
Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine
Learning Algorithms.
• Astha Ratley Mrs, Jasmine Minj Mrs, Pooja Patre illustrates Leukemia Disease Detection and Classification
Using Machine learning Approaches
• Saif Ali, Sai fur Rehman, Aneeqa Tamveer, Azhar Hussain presents Identification of cancer disease using image-
processing approaches.
• Naresh Khuriwal, Nidhi Mishra predicts Breast Cancer Detection From Histopathological Images Using Deep
Learning.
• Mobeen-ur-Rehman , Sharzil Haris Khan, Zeeshan Abbas , S.M. Danish Rizvi describes Classification of
Diabetic Retinopathy Images Based on Customised CNN Architecture.
• Amjad Khana , Zahid Ansari shows Identification of Lung Cancer Using Convolutional Neural Networks Based
Classification.
• Ravva Sai Sanketh, Dr. M Madhu Bala, Panati Viswa Narendra Reddy, G V S Phani Kumar presents Melanoma
Disease Detection Using Convolutional Neural Networks.
• Mohammad Akter Hossain, Mubtasim Islam Sabik illustrates . Leukemia Detection Mechanism through
Microscopic Image and ML Techniques.
• Deepika Kumar, Nikita Jain describes Automatic Detection of White Blood Cancer from Bone Marrow
Microscopic Images Using Convolutional Neural Networks.
• Supriya Mandal, Vani Daivajna illustrates Machine Learning based System for Automatic Detection of
Leukemia Cancer Cell.
• Preeti Jagadev, Dr. H.G. Viran presents Detection of Leukemia and its Types using Image Processing and
Machine Learning.
• Ramesh , Prashanth P, Sampath Kumar illustrated the python programming for the detection of blood cancer and
project deployment.
• N.H.Abd Halim; M.Y. Mashor shows Nucleus segmentation technique for acute Leukemia using convolutional
neural network.
• Adnan KhashmanHayder Hassan Abba illustrated the method of Acute Lymphoblastic Leukemia Identification
Using Blood Smear Images and a Neural Classifier.
• Himali P. Vaghela describes Leukemia Detection using Digital Image Processing Techniques for leukimia
project deployment.
III. METHODOLOGY
Figure 2 : Block diagram of blood cancer detection using ML
© IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 252
IARJSET ISSN (O) 2393-8021, ISSN (P) 2394-1588
International Advanced Research Journal in Science, Engineering and Technology
ISO 3297:2007 Certified Impact Factor 7.105 Vol. 9, Issue 6, June 2022
DOI: 10.17148/IARJSET.2022.9639
For training the module 1000's of images of cancerous and non cancerous sample are collected and stored in a file. The
file is divided into two parts training datasets and testing data sets training data sets is used to train the machine learning
module .It consists of cancerous and non cancerous images labelled respectively. These images will be fed into machine
learning module, and trained module is extracted.
Figure 3 : Diagnosis of Leukemia
The diagnosis of cancer starts with the collection of blood sample at the laboratory, the microscopy image is then passed
into trained neural module for diagnosis. The trained machine learning module will give output whether the sample is
cancerous or non cancerous based on prediction value.
For training the module 1000's of images of cancerous and non cancerous sample are collected and stored in a file.
Training data sets is used to train the machine learning module. It consists of cancerous and non cancerous images labelled
respectively. These images will be fed into machine learning module, and trained module is extracted. The neural machine
learning module is created using keras library and python in google collab editor .
IV. CONCLUSION
Due to modern lifestyle, pollution and other factors cancer is becoming more of common disease. With conventional
methods of detection of cancer takes time because transport of sample tissue (biopsy) to a cancer diagnosing facility ,since
cancer is fast spreading disease early treatment will likely increase the chances of survival of cancer patient. With the
help of machine learning ,results are fast and accurate which helps in early detection and also the diagnosing process can
be done at low cost.
REFERENCES
[1]. Raghav Kandhari, Anupama Bhan, Parth Bhatnagar “Computer based diagnosis of Leukemia in blood smear
images” ,IEEE-2021
[2]. Patil Babaso S, S.K. Mishra, Aparna Junnarkar “Leukemia Diagnosis Based on Machine Learning Algorithms”,IEEE-
2020
[3]. N. H. M. Daud , R. A. A. Raof, M. K. Osman, N. H. Harun “Segmentation Technique for Nucleus Detection in Blood
Images for Chronic Leukaemia”,ICED-2020
[4]. Dharani T, Hariprasath S “Diagnosis of Leukemia and its types Using Digital Image Processing Techniques”,IEEE-
2018
[5]. Atharva Bankar, Kewal Padamwar, Aditi Jahagirdar “Symptom Analysis using a Machine Learningapproach for
Early Stage Lung Cancer” IEEE-2020
[6]. Saif Ali, Saif Rehman, Azhar Hussain, Anneqa Tanveer "Identification of Cancer diseases using image processing"
IEEE 2020
[7]. Astha Ratley Mrs. Jasmine Minj Mrs. Pooja Patre “Leukemia Disease Detection and Classification Using Machine
Learning Approaches”
© IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 253
IARJSET ISSN (O) 2393-8021, ISSN (P) 2394-1588
International Advanced Research Journal in Science, Engineering and Technology
ISO 3297:2007 Certified Impact Factor 7.105 Vol. 9, Issue 6, June 2022
DOI: 10.17148/IARJSET.2022.9639
[8]. Subhash Rajpurohit Sanket Patil Nitu Choudhary, Shreya Gavasane Prof. Pranali Kosamkar ‘Identification of Acute
Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms’
[9]. Ravva Sai Sanketh, Dr. M Madhu Bala, Panati Viswa Narendra Reddy, G V S Phani Kumar “Melanoma Disease
Detection Using Convolutional Neural Networks”
[10]. Amjad Khana , Zahid Ansarib “Identification of Lung Cancer Using Convolutional Neural Networks Based
Classificatio”,IEEE-2021
[11]. Naresh Khuriwal, Nidhi Mishra,” Breast Cancer Detection From Histopathological Images Using Deep
Learning ”,IEEE-2018
[12]. Mobeen-ur-Rehman , Sharzil Haris Khan, Zeeshan Abbas , S.M. Danish Rizvi ,”Classification of Diabetic
Retinopathy Images Based on Customised CNN Architecture ”,2018 [13] Mohammad Akter Hossain, Mubtasim Islam
Sabik, “Leukemia Detection Mechanism through microscopic Image and ML Techniques”,IEEE-2020
[14]. Deepika Kumar, Nikita Jain, “Automatic Detection of White Blood Cancer from Bone Marrow.Microscopic Images
Using Convolutional Neural Networks”,IEEE-2020
[15]. Supriya Mandal, Vani Daivajna, “Machine Learning based System for Automatic Detection of Leukemia Cancer
Cell”, IEEE-2020
[16]. Preeti Jagadev, Dr. H.G. Virani , “Detection of Leukemia and its Types using Image Processing and Machine
Learning,” ICEI-2017
© IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 254
