Haleema Ehsan
Research Subjects
Research Interests
Haleema Ehsan has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Aisha Sikandar
Computer Science Department, GGPGC No.1, Abbottabad 22020, Pakistan
1 shared publication - Mehnoor Ahsan
Computer Science Department, GGPGC No.1, Abbottabad 22020, Pakistan
1 shared publication - Riaz Ahmad
Computer Science Department, Shaheed Benazir Bhutto University, Upper Dir 00384, Pakistan
1 shared publication - Saeeda Naz
Computer Science Department, Govt Girls Postgraduate College No.1, Abbottabad, Pakistan
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 6 January 2023
- Published by MDPI AG in Information
- Vol. 14 (1) , 36
A Deep Learning Approach for Diabetic Foot Ulcer Classification and Recognition
Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on a deep learning framework show promising results. In this paper, we present several end-to-end CNN-based deep learning architectures, i.e., AlexNet, VGG16/19, GoogLeNet, ResNet50.101, MobileNet, SqueezeNet, and DenseNet, for infection and ischemia categorization using the benchmark dataset DFU2020. We fine-tune the weight to overcome a lack of data and reduce the computational cost. Affine transform techniques are used for the augmentation of input data. The results indicate that the ResNet50 achieves the highest accuracy of 99.49% and 84.76% for Ischaemia and infection, respectively.
- 6 January 2023
- Published by MDPI AG in Information
- Vol. 14 (1) , 36
Top Cited PapersOpen AccessA Deep Learning Approach for Diabetic Foot Ulcer Classification and Recognition
Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on a deep learning framework show promising results. In this paper, we present several end-to-end CNN-based deep learning architectures, i.e., AlexNet, VGG16/19, GoogLeNet, ResNet50.101, MobileNet, SqueezeNet, and DenseNet, for infection and ischemia categorization using the benchmark dataset DFU2020. We fine-tune the weight to overcome a lack of data and reduce the computational cost. Affine transform techniques are used for the augmentation of input data. The results indicate that the ResNet50 achieves the highest accuracy of 99.49% and 84.76% for Ischaemia and infection, respectively.