Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.16221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916141989888000 |
|---|---|
| author | Vu, Ha Anh |
| author_facet | Vu, Ha Anh |
| contents | This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The CNN model is trained and evaluated on a dataset of medical images, with augmentation and data generators utilized to enhance model generalization. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16221 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging Vu, Ha Anh Image and Video Processing Computer Vision and Pattern Recognition 62H30 I.4.9 This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The CNN model is trained and evaluated on a dataset of medical images, with augmentation and data generators utilized to enhance model generalization. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare. |
| title | Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 62H30 I.4.9 |
| url | https://arxiv.org/abs/2402.16221 |