A Lightweight Convolutional Neural Network for White Blood Cells Classification
| Autor: | Md. Alif Rahman Ridoy, Md. Rabiul Islam |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 0206 medical engineering Process (computing) Word error rate Pattern recognition 02 engineering and technology 020601 biomedical engineering Convolutional neural network Identification (information) Categorization Binary classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business |
| Zdroj: | 2020 23rd International Conference on Computer and Information Technology (ICCIT). |
| Popis: | Our immune system is a complex network that consists of cells, tissues, and organs that operates concurrently to shield our body from millions of diseases, causing bacteria, parasites, and viruses. For the identification of different kinds of hematological disorders, the accurate identification of various White blood cells (WBC) is necessary for classification purposes. Most of the diseases can be diagnosed by the numbers and sizes of white blood cells found in a blood smear. A drastic change in a particular WBC count relative to the standard range provides us a hint about being attacked by distinct enzyme. As the incorrect segmentation of cells leads to inaccurate disease detection, it demands utmost significance that this process is performed in the best effective way. Still now, in many medical centers the detection and categorization of WBCs is performed manually by experts. As there remains a great probability of error due to manual classification, automatic systems should be designed in such a way that there will be a very minimal error rate as compared to the manual. With this aim, in this paper, a renowned methodology named Deep learning is proposed to conduct the whole classification process automatically applying an improved lightweight convolutional neural network which has been implemented for both multiclass and binary classification with an accuracy rate of 98.63% and 91.95% respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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