# Automated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks

arXiv: https://arxiv.org/abs/2208.08992 · HTML: https://mlsanity.com/paper/2208.08992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 30 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Early Prediction of Heart Disease Using PCA and Hybrid Genetic Algorithm with k-Means](https://mlsanity.com/paper/2101.00183/) (2021)
- [Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques](https://mlsanity.com/paper/2203.01394/) (2022)

## Built on by (most cited first, 2 of 2)

- [Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images](https://mlsanity.com/paper/2310.13108/) (2023)
- [DVS: Blood cancer detection using novel CNN-based ensemble approach](https://mlsanity.com/paper/2410.05272/) (2024)

## Abstract (arXiv)

An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis software to provide real-time support to diagnose ALL subtypes from microscopic blood smears images.