Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review
The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagn...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-07-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31420 |
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| author | Mohammad Faiz Bakkanarappa Gari Mounika Mohd Akbar Swapnita Srivastava |
| author_facet | Mohammad Faiz Bakkanarappa Gari Mounika Mohd Akbar Swapnita Srivastava |
| author_sort | Mohammad Faiz |
| collection | DOAJ |
| description | The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models. |
| format | Article |
| id | doaj-art-453dc03b320a472c8d86e3218a354771 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-453dc03b320a472c8d86e3218a3547712025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-07-0113e31420e3142010.14201/adcaij.3142036898Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive ReviewMohammad Faiz0Bakkanarappa Gari Mounika1Mohd Akbar2Swapnita Srivastava3School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaDepartment of Computer Science and Engineering, Integral University, Lucknow, IndiaDepartment of Computer Science and Engineering, Integral University, Lucknow, IndiaThe medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31420acute lymphoblastic leukemiamachine learningdeep learningblood smear images |
| spellingShingle | Mohammad Faiz Bakkanarappa Gari Mounika Mohd Akbar Swapnita Srivastava Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review Advances in Distributed Computing and Artificial Intelligence Journal acute lymphoblastic leukemia machine learning deep learning blood smear images |
| title | Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review |
| title_full | Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review |
| title_fullStr | Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review |
| title_full_unstemmed | Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review |
| title_short | Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review |
| title_sort | deep and machine learning for acute lymphoblastic leukemia diagnosis a comprehensive review |
| topic | acute lymphoblastic leukemia machine learning deep learning blood smear images |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31420 |
| work_keys_str_mv | AT mohammadfaiz deepandmachinelearningforacutelymphoblasticleukemiadiagnosisacomprehensivereview AT bakkanarappagarimounika deepandmachinelearningforacutelymphoblasticleukemiadiagnosisacomprehensivereview AT mohdakbar deepandmachinelearningforacutelymphoblasticleukemiadiagnosisacomprehensivereview AT swapnitasrivastava deepandmachinelearningforacutelymphoblasticleukemiadiagnosisacomprehensivereview |