Publications by authors named "Mohamed Lachgar"
Article Synopsis
- Deep learning struggles with detecting and diagnosing medical image anomalies due to data imbalance, variability, and complexity, particularly in skin diseases that show significant differences in appearance and texture.
- A new hybrid architecture combining wavelet decomposition with EfficientNet models has been developed to address these challenges, utilizing advanced techniques for data augmentation, loss functions, and optimization.
- The proposed model demonstrated impressive accuracy rates of 94.7% and 92.2% when tested on the HAM10000 and ISIC2017 datasets, respectively.
Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.
Comput Math Methods Med
February 2022
Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients.
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