arXiv:2406.18568v1 Announce Type: cross Abstract: Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow and peripheral blood. Manual diagnosis of this disease is a tedious and time-consuming operation, making it difficult for professionals to accurately examine blast cell characteristics. To address this difficulty, researchers use deep learning and machine learning. In this paper, a ResNet-based feature extractor is utilized to detect ALL, along with a variety of feature selectors and classifiers. To get the best results, a variety of transfer learning models, including the Resnet, VGG, EfficientNet, and DensNet families, are used as deep feature extractors. Following extraction, different feature selectors are used, including Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. After feature qualification, a variety of classifiers are used, with MLP outperforming the others. The recommended technique is used to categorize ALL and HEM in the selected dataset which is C-NMC 2019. This technique got an impressive 90.71% accuracy and 95.76% sensitivity for the relevant classifications, and its metrics on this dataset outperformed others.
The article “Acute lymphoblastic leukemia (ALL) severity determination using deep learning and machine learning” explores the use of advanced technologies to improve the diagnosis of ALL, a type of blood cancer. Manual diagnosis of ALL is time-consuming and challenging, leading researchers to turn to deep learning and machine learning techniques. The study utilizes a ResNet-based feature extractor, along with various feature selectors and classifiers, to detect ALL accurately. Transfer learning models such as Resnet, VGG, EfficientNet, and DensNet families are employed as deep feature extractors, followed by different feature selectors and classifiers. The MLP classifier proves to be the most effective. The recommended technique achieves an impressive 90.71% accuracy and 95.76% sensitivity in categorizing ALL and HEM in the C-NMC 2019 dataset, outperforming other methods. Overall, this research demonstrates the potential of deep learning and machine learning in improving the diagnosis of ALL.
Exploring Deep Learning and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis
Acute lymphoblastic leukemia (ALL) is a severe form of cancer that affects white blood cells. The severity of the disease is determined by the presence and ratios of blast cells, which are abnormal white blood cells, in both the bone marrow and peripheral blood. However, the manual diagnosis of ALL can be a tedious and time-consuming process, leading to difficulties in accurately examining blast cell characteristics. To address these challenges, researchers have turned to deep learning and machine learning techniques.
In a recent paper, researchers utilize a ResNet-based deep learning model as a feature extractor to detect ALL. To achieve the best results, a variety of transfer learning models, such as Resnet, VGG, EfficientNet, and DensNet families, are used as deep feature extractors. These models have been pre-trained on large datasets and possess the ability to extract meaningful features from medical images.
Once the features are extracted, different feature selection techniques are employed to identify the most relevant and informative features for classification. Some of the feature selectors used in this study include Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. These techniques help in reducing the dimensionality of the data and improving the performance of the classification models.
After feature qualification, various classifiers are tested to categorize ALL and differentiate it from other conditions like HEM. Among the classifiers experimented with in this study, the Multi-Layer Perceptron (MLP) outperforms the others. MLP is a feed-forward neural network that can effectively handle non-linear relationships between the input features and the target variable. Its performance in accurately classifying ALL showcases its potential as a valuable tool in leukemia diagnosis.
The proposed technique is evaluated on a selected dataset called C-NMC 2019, which consists of both ALL and HEM samples. The results of this study demonstrate the effectiveness of the approach, with an impressive 90.71% accuracy and 95.76% sensitivity for the relevant classifications. These metrics indicate that the technique outperforms other methods when applied to this specific dataset.
The use of deep learning and machine learning in diagnosing acute lymphoblastic leukemia presents a considerable advancement in the field of cancer diagnosis. By automating the process and leveraging powerful models, medical professionals can save time and improve the accuracy of their assessments. Furthermore, the combination of various transfer learning models, feature selection techniques, and classification algorithms opens up possibilities for further research and optimization of the diagnostic process.
The paper discussed here focuses on the use of deep learning and machine learning techniques to address the challenges in diagnosing acute lymphoblastic leukemia (ALL). ALL is a type of blood cancer, and its severity is determined by the presence and ratios of abnormal white blood cells called blast cells in both bone marrow and peripheral blood.
The manual diagnosis of ALL is a time-consuming and tedious process, which can make it difficult for medical professionals to accurately examine blast cell characteristics. To overcome this challenge, the researchers in this study propose the use of a ResNet-based feature extractor combined with various feature selectors and classifiers.
To achieve the best results, the researchers employ a range of transfer learning models, including the Resnet, VGG, EfficientNet, and DensNet families, as deep feature extractors. Transfer learning allows the models to leverage pre-trained networks on large datasets, which can help improve the accuracy of the classification task.
After extracting features, different feature selectors are applied, including Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. These selectors help identify the most relevant features that contribute to the accurate classification of ALL.
Once the features are qualified, a variety of classifiers are employed, with the Multi-Layer Perceptron (MLP) outperforming the others. MLP is a type of artificial neural network that is well-suited for classification tasks.
The proposed technique is then applied to categorize ALL and HEM (normal hematopoietic cells) in the selected dataset, which is the C-NMC 2019 dataset. The results obtained using this technique are impressive, with an accuracy of 90.71% and a sensitivity of 95.76% for the relevant classifications. These metrics outperformed other techniques on this dataset, indicating the effectiveness of the proposed approach.
In terms of future directions, this research highlights the potential of using deep learning and machine learning techniques for the automated diagnosis of ALL. Further exploration could involve the application of these techniques to larger and more diverse datasets to validate the findings. Additionally, the integration of other types of data, such as genetic information or clinical data, could enhance the accuracy and predictive power of the classification models. Overall, this study demonstrates the promising role of artificial intelligence in improving the efficiency and accuracy of leukemia diagnosis, potentially leading to better patient outcomes.
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