Abstract
This paper describes our approach for the classification of normal versus malignant cells in B-ALL white blood cancer microscopic images: ISBI 2019—classification of leukemic B-lymphoblast cells from normal B-lymphoid precursors from blood smear microscopic images. We leverage a state of the art convolutional neural network pretrained with the ImageNet dataset and applied several data augmentation and hyperparameters optimization strategies. Our method obtains an F1 score of 0.83 for the final test set in the competition.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Bioengineering |
| Publisher | Springer |
| Pages | 141-147 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Publication series
| Name | Lecture Notes in Bioengineering |
|---|---|
| ISSN (Print) | 2195-271X |
| ISSN (Electronic) | 2195-2728 |
Keywords
- B-lymphoblast cell
- B-lymphoid
- Blood cancer
- Blood smear
- Convolutional neural networks
ASJC Scopus subject areas
- Biotechnology
- Bioengineering
- Applied Microbiology and Biotechnology
- Biomedical Engineering
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Cite this
Khan, M. A., & Choo, J. (2019). Classification of cancer microscopic images via convolutional neural networks. In Lecture Notes in Bioengineering (pp. 141-147). (Lecture Notes in Bioengineering). Springer. https://doi.org/10.1007/978-981-15-0798-4_15