Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
- Title
- Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
- Creator
- Thomas S.; Vijaylakshmi S.
- Description
- Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322. 2022 IEEE.
- Source
- 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1053-1055.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep Convolutional neural networks; microscopic images; Transfer learning
- Coverage
- Thomas S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Lavasa, 412112, India; Vijaylakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Lavasa, 412112, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166543789-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Thomas S.; Vijaylakshmi S., “Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20255.