International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
A study on techniques to detect and classify acute lymphoblastic leukemia using deep learning. Shaik Abdul Hameed1, Sai Nikhil G2, Pranav Sai Y3, Sathya Sreekar D4, Veerendra Reddy Y5 1Associate Professor, Dept. of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and
Technology, Hyderabad, India
2,3,4,5 Student, Dept. of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and
Technology, Hyderabad, India ---------------------------------------------------------------------***--------------------------------------------------------------------2. RELATED WORK Abstract - Cancer starts when cells in the body begin to grow out of control. Leukemia is a cancer that affects the body's blood-forming tissues, lymphatic system, and bone marrow. White blood cells are typically involved in Leukemia (WBC). When an individual has Leukemia, their bone marrow makes an excessive amount of dysfunctional WBC. While Acute Lymphoblastic Leukemia (ALL) predominantly affects children but is not limited to them and can also develop in adults. As a widely occurring cancer, the accurate diagnosis of ALL necessitates costly, invasive, and time-intensive diagnostic tests. The use of PBS images for diagnosing ALL plays a crucial role in the initial screening of cancer cases versus non-cancer cases. Our project aims to Automate the process of detection of Acute Lymphoblastic Leukemia (ALL) using Peripheral Blood Smear (PBS) images and provide a channel between patients and doctors for consultancy regarding the diagnosis process.
In [1], Naina Sharma, Ankit Mukopadhyay, Aman Shrivastava, and Aman Garg proposed a system to help identify Leukemia through pictures of blood cells. The proposed system is supposed to be more accurate than present physicians. A system is proposed due to the scrutiny of blood or bone marrow pictures being negative for detection and very time-consuming. The authors propose using segment-stained peripheral blood smears using colorbased clustering to divide a cell into a nucleus and cytoplasm. Then, SVM is used to differentiate the types of WBC and use CNN with the last layer of the CNN fed to a Random Forest algorithm to categorize the WBC. In [2], C. T. Tchapga, M. Thomas Attia, A. T. Kouanou, T. F. Fonzin, P. K. Fogang, M. Brice Anicet, T. Daniel proposed how ML algorithms can be used with big data using Apache Spark Framework and how to classify biomedical images using the machine learning algorithms. Apache Spark overcomes the limitations of the Hadoop framework by being faster (by 100 times in memory), making data for iterations, queries, and loading, and supporting SQL Query over Map Reduce. The authors also suggest a two-step process: the first is creating an algorithm using labelled images, and the second is classification done through unlabeled images.
Key Words: Acute Lymphoblastic leukemia, peripheral Blood smear, Lymphatic system, CNN (Convolution Neural Network), CBC (Complete Blood Count), Differential Leukocyte Count (DLC)
1. INTRODUCTION Over 900,000 people are diagnosed with leukemia each year, sometimes known as blood cancer, however many people are unaware of the risks associated with such frequently fatal illnesses. Most of blood cancers are uncommon, lifethreatening diseases that only affect small patient populations. Patients may have a sense of abandonment as a result of leukemia's rarity and find it challenging to locate the support and information they require.
In [3], A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti propose a system to detect ALL based on the adaptive un-sharpening of the peripheral blood smear images. The authors propose using adaptive un-sharpening using Computer Aided Diagnosis (CAD). Adaptive unsharpening is a process used to increase the focus of an image until a certain threshold. The system used for ALL detection performs adaptive un-sharpening initially and then performs the classification. After experimenting with 260 images of WBC, the authors identified the accuracy of the detection model to be 96.84%. The only con of the model is that the model is a detection model but not a classification model.
If treatment for acute Leukemia is not started promptly, the patient may pass away from the condition within a few months. Any cancer must be detected early to receive prompt treatment and improve survival rates. Individuals who are ill cannot waste time because they require quick attention. We need systems that quickly use the most recent technological advancements and accurately analyze blood samples. Early detection of Acute Lymphoblastic Leukemia (ALL) symptoms in individuals can considerably improve their chances of survival.
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In [4], N. Mahmood, S. Shahid, T. Bakhshi, S. Riaz, H. Ghufran, and M. Yaqoob proposed the significance of clinical data and phenotypic data, i.e., environmental conditions to detect Acute Lymphoblastic Leukemia. They used different models
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