Leukemia is one of the most common and death-threatening types of cancer that
threaten human life. Medical data from some of the patient’s critical
parameters contain valuable information hidden among these data. On this
subject, deep learning can be used to extract this information. In this paper,
AutoEncoders have been used to develop valuable features to help the precision
of leukemia diagnosis. It has been attempted to get the best activation
function and optimizer to use in AutoEncoder and designed the best architecture
for this neural network. The proposed architecture is compared with this area’s
classical machine learning models. Our proposed method performs better than
other machine learning in precision and f1-score metrics by more than 11%.

Unlocking Hidden Information in Leukemia Data using Deep Learning

Leukemia, a common and life-threatening form of cancer, presents a complex challenge for medical professionals. One of the key difficulties lies in uncovering valuable information buried within critical patient data. Deep learning, a multi-disciplinary approach combining computer science and medicine, offers a promising solution to extract this hidden knowledge. In this study, researchers have utilized AutoEncoders, a deep learning technique, to develop useful features that enhance the precision of leukemia diagnosis.

The crux of this research lies in identifying the optimal activation function and optimizer for the AutoEncoder neural network. By meticulously experimenting with various configurations, the researchers determined the best combination that yielded the highest performance. Additionally, they designed an optimal architecture for the neural network, further enhancing the accuracy of the proposed method.

For a comprehensive evaluation, the performance of the proposed architecture was compared against classical machine learning models commonly used in this field. The results were indeed remarkable. The proposed method showcased superior performance in precision and f1-score metrics, surpassing other machine learning models by a significant margin of 11%.

This collaborative effort bridges the worlds of medicine and deep learning, proving the potential of multi-disciplinary approaches in enhancing medical diagnosis and treatment. Utilizing deep learning techniques like AutoEncoders allows medical professionals to leverage hidden patterns present in patient data, leading to more accurate and timely leukemia diagnoses.

Looking ahead, the integration of deep learning into medical practices holds immense promise. It is crucial to continue advancing research in this area to refine algorithms and architectures further. By incorporating more diverse datasets and exploring various deep learning techniques, we can uncover even more valuable insights that could revolutionize cancer diagnostics and treatment strategies.

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