دانلود مقاله تشخیص سرطان خون شناختی با استفاده از گلبول های سفید سرطانی

خلاصه
معرفی
بررسی ادبیات
مواد و روش ها
بحث
نتیجه
اختصارات
در دسترس بودن داده ها
تضاد علاقه
منابع

Abstract
Introduction
Literature Review
Materials and Methods
Discussion
Conclusion
Abbreviations
Data Availability
Conflicts of Interest
References

چکیده
لنفوم و لوسمی از سندرم‌های کشنده سرطان هستند که باعث بیماری‌های دیگر می‌شوند و همه گروه‌های سنی اعم از زن و مرد را درگیر می‌کنند و سرطان خون فاجعه‌بار و کشنده باعث افزایش نسبت مرگ و میر بیشتر می‌شود. لنفوم و لوسمی هر دو با آسیب و افزایش لنفوسیت‌های نابالغ، مونوسیت‌ها، نوتروفیل‌ها و سلول‌های ائوزینوفیل مرتبط هستند. بنابراین، در بخش بهداشت، پیش‌بینی و درمان زودهنگام سرطان خون یک مسئله اصلی برای میزان بقا است. امروزه تکنیک‌های دستی مختلفی برای تجزیه و تحلیل و پیش‌بینی سرطان خون با استفاده از گزارش‌های پزشکی میکروسکوپی تصاویر گلبول‌های سفید وجود دارد که برای پیش‌بینی بسیار ثابت است و باعث نسبت عمده مرگ و میر می‌شود. پیش‌بینی و تجزیه و تحلیل دستی ائوزینوفیل‌ها، لنفوسیت‌ها، مونوسیت‌ها و نوتروفیل‌ها بسیار دشوار و زمان‌بر است. در مطالعات قبلی، آنها از تکنیک‌های یادگیری عمیق و یادگیری ماشینی متعددی برای پیش‌بینی سرطان خون استفاده کردند، اما هنوز محدودیت‌هایی در این مطالعات وجود دارد. بنابراین، در این مقاله، ما مدلی از یادگیری عمیق با قدرت یادگیری انتقال و افراط در تکنیک‌های پردازش تصویر برای بهبود نتایج پیش‌بینی پیشنهاد می‌کنیم. مدل یادگیری انتقال پیشنهادی با پردازش تصویر، سطوح مختلفی از پیش‌بینی، تجزیه و تحلیل و روش‌های یادگیری را در بر می‌گیرد و معیارهای یادگیری متفاوتی مانند میزان یادگیری و دوره‌ها را به کار می‌گیرد. مدل پیشنهادی از مدل‌های یادگیری انتقال متعدد با پارامترهای متفاوت برای هر مدل و تکنیک‌های ابری برای انتخاب بهترین مدل پیش‌بینی استفاده کرد و مدل پیشنهادی از مجموعه گسترده‌ای از تکنیک‌ها و روش‌های عملکردی برای پیش‌بینی گلبول‌های سفید خونی که باعث سرطان می‌شوند برای ترکیب پردازش تصویر استفاده کرد. تکنیک. بنابراین، پس از رویه‌های گسترده AlexNet، MobileNet و ResNet با هر دو پردازش تصویر و بدون تکنیک‌های پردازش تصویر با معیارهای یادگیری متعدد، تکانه نزولی گرادیان تصادفی گنجانده شده با AlexNet با بالاترین دقت پیش‌بینی 97.3% بهتر عمل می‌کند و نرخ طبقه‌بندی اشتباه با تکنیک پردازش تصویر %2.7 است.  مدل پیشنهادی نتایج خوبی به دست می دهد و می تواند برای تشخیص هوشمند سرطان خون با استفاده از ائوزینوفیل ها، لنفوسیت ها، مونوسیت ها و نوتروفیل ها استفاده شود.

Abstract

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Introduction

Leukemia and lymphoma are the most frequent kinds of blood cancer in people of all ages, particularly young people. This abnormal situation is induced by red blood cell proliferation and immature growth, which can harm red blood cells, bone marrow, and the immune system [1]. Leukemia accounts for more than 3.5% of new cancer cases in the United States, with over 50,000 new cases diagnosed in 2018 [2]. Cancerous lymphoblasts in the blood travel to other organs, including the heart, brain, lungs, and arteries, before spreading to important tissues throughout the body. Red blood cells are normally in charge of transporting oxygen from the heart to all organs. They make up half of the total blood volume. White blood cells, on the other hand, serve an important role in the human immune system, serving as the first line of protection against a variety of diseases and disorders [3]. As a result, accurately identifying these white blood cells is crucial in understanding the symptoms of the problem. Their categorization is determined by their cytoplasmic composition. Changes in lymphocytes, a kind of white blood cell, cause acute lymphoblastic leukemia [4]. Acute or chronic leukemia are the two types of leukemia. Without treatment, the typical recovery period for acute myeloid leukemia is roughly three months; however, the time of appearance of chronic leukemia is longer than that of acute leukemia. Chronic lymphoblastic leukemia is the most common kind of acute leukemia, accounting for around 25% of all juvenile malignancies [5, 6]. Early detection of leukemia and lymphoma has always been difficult for researchers, clinicians, and hematologists. Leukemia symptoms include enlarged lymph nodes, paleness, fever, and weight loss, although these symptoms can also be caused by other diseases [7]. Because of the moderate nature of the symptoms, diagnosing leukemia and lymphoma in its early stages is challenging. PBS microscopic assessment is the most often used leukemia and lymphoma diagnostic approach, while the gold standard for leukemia and lymphoma diagnosis only entails obtaining and analyzing white blood cell samples [8]. Several research have utilized machine learning and deep learning and machine diagnostics approaches to laboratory image processing during the last two decades in the hopes of pushing the boundaries of late diagnosis of leukemia and lymphoma and establishing their subtypes [9]. In this research, blood smear pictures were evaluated to diagnose, distinguish, and count cells in distinct kinds of leukemia and lymphoma [10].

Conclusion

The early detection of blood cancer using white blood cells can help meritoriously in its cure. The study's proposed framework consists of three transfer learning models AlexNet, MobileNet, and ResNet empowered with SGDM, ADAM, and RMSPROP. The proposed framework applies all transfer learning models with varying learning rates effectively to white blood cancerous cells for early classification. To enhance the results, the proposed study used image processing techniques incorporating transfer learning and achieved the highest CA of 97.3% and 2.7% MCR. All experiments in the proposed study are comprehensively explained with respect to every model training and testing phase. This study helps health 5.0 to predict blood cancer in its early stages for early treatment. Furthermore, in the future, federated machine learning using the fed average technique will play a major role in better early prediction of blood cancer empowered with fuzzed machine learning and deep learning techniques will also apply more statistical techniques such as ANOVA and Chi-Square.