DVS: Blood cancer detection using novel CNN-based ensemble approach
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2024
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Abstract
Blood cancer can only be diagnosed properly if it is detected early. Each
year, more than 1.24 million new cases of blood cancer are reported worldwide.
There are about 6,000 cancers worldwide due to this disease. The importance of
cancer detection and classification has prompted researchers to evaluate Deep
Convolutional Neural Networks for the purpose of classifying blood cancers. The
objective of this research is to conduct an in-depth investigation of the
efficacy and suitability of modern Convolutional Neural Network (CNN)
architectures for the detection and classification of blood malignancies. The
study focuses on investigating the potential of Deep Convolutional Neural
Networks (D-CNNs), comprising not only the foundational CNN models but also
those improved through transfer learning methods and incorporated into ensemble
strategies, to detect diverse forms of blood cancer with a high degree of
accuracy. This paper provides a comprehensive investigation into five deep
learning architectures derived from CNNs. These models, namely VGG19,
ResNet152v2, SEresNet152, ResNet101, and DenseNet201, integrate ensemble
learning techniques with transfer learning strategies. A comparison of
DenseNet201 (98.08%), VGG19 (96.94%), and SEresNet152 (90.93%) shows that DVS
outperforms CNN. With transfer learning, DenseNet201 had 95.00% accuracy, VGG19
had 72.29%, and SEresNet152 had 94.16%. In the study, the ensemble DVS model
achieved 98.76% accuracy. Based on our study, the ensemble DVS model is the
best for detecting and classifying blood cancers.
| Reference Key |
li2024dvs
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|---|---|
| Authors | Md Taimur Ahad; Israt Jahan Payel; Bo Song; Yan Li |
| Journal | arXiv |
| Year | 2024 |
| DOI |
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