Name
Title | ||
|---|---|---|
Pixels To Classes: Intelligent Learning Framework For Multiclass Skin Lesion Localization And Classification |
Abstract | ||
|---|---|---|
A novel deep learning framework is proposed for lesion segmentation and classification. The proposed technique incorporates two primary phases. For lesion segmentation, Mask recurrent convolutional neural network (MASK R-CNN) based architecture is implemented. In this architecture, Resnet50 along with feature pyramid network (FPN) is utilized as a backbone. Later, fully connected layer-based features are mapped for the final mask generation. In the classification phase, 24-layered convolutional neural network architecture is designed, which performs activation based on the visualization of higher features. Finally, best CNN features are provided to softmax classifiers for final classification. Three datasets (i.e. PH2, ISBI2016, and ISIC2017) are utilized for the validation of the segmentation process, whilst HAM10000 dataset is utilized for the classification. From the results, it is concluded that the proposed method outperforms several existing techniques, based on the selected set of parameters including sensitivity (85.57%), precision (87.01%), F1- Score (86.28%), and accuracy (86.5%). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compeleceng.2020.106956 | COMPUTERS & ELECTRICAL ENGINEERING |
Keywords | DocType | Volume |
Skin Cancer, Lesion Segmentation, CNN Architecture, Recognition | Journal | 90 |
ISSN | Citations | PageRank |
0045-7906 | 2 | 0.38 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Attique Khan | 1 | 69 | 11.89 |
| yudong zhang | 2 | 1334 | 90.44 |
| Muhammad Sharif | 3 | 5 | 1.10 |
| Tallha Akram | 4 | 29 | 3.88 |