Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks.

BACKGROUND: With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in the early stages. OBJECTIVE: The diagnosis of skin cancer is becoming a challenge to dermatologists as an abnormal lesion looks like an ordinary nevus at the initial stages. Therefore, early identification of lesions (origin of skin cancer) is essential and helpful for treating skin cancer patients effectively. The enormous development of automated skin cancer diagnosis systems significantly supports dermatologists. METHODS: This paper performs a classification of skin cancer by utilising various deep-learning frameworks after resolving the class Imbalance problem in the ISIC-2019 dataset. A fine-tuned ResNet-50 model is used to evaluate the performance of original data, augmented data, and after by adding the focal loss. Focal loss is the best technique to solve overfitting problems by assigning weights to hard misclassified images. RESULTS: Finally, augmented data with focal loss is given a good classification performance with 98.85% accuracy, 95.52% precision, and 95.93% recall. Matthews Correlation coefficient (MCC) is the best metric to evaluate the quality of multi-class images. It has given outstanding performance by using augmented data and focal loss.

Application of Deep Learning in the Diagnosis of Alzheimer's and Parkinson's disease-A Review.

Most neurodegenerative diseases such as Alzheimer's and Parkinson's are life-threatening, critical, and incurable affecting mainly the elderly population. Early diagnosis is challenging as disease phenotype is very crucial for predicting, preventing the progression, and effective drug discovery. In the last few years, Deep learning (DL) based neural networks are the state-of-the-art models deployed in industries and academics across different areas like natural language processing, image analysis, speech recognition, audio classification, and many more. It has been slowly realized that they have a high potential in medical image analysis and diagnostics and medical management in general. As this field is vast and expanding rapidly, we have put focused on existing DL-based models to detect Alzheimer's and Parkinson's in particular. This study gives a summary of related medical examinations for these diseases. Frameworks and applications of many deep learning models have been discussed. We have given precise notes on pre-processing techniques used by various studies for MRI image analysis. An overview of the application of DL-based models in different stages of medical image analysis has been conferred. It has been realized from the review that more studies are focused on Alzheimer's compared to Parkinson's disease. Additionally, we have tabulated the various public datasets available for these diseases. We have highlighted the potential use of a novel biomarker for the early diagnosis of these disorders. Also, some challenges and issues in implementing deep learning techniques for the detection of these diseases have been addressed. Finally, we concluded with some directions for future research regarding deep learning in the diagnosis of these diseases.