ABSTRACT
Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.
ABSTRACT
OBJECTIVE: This study aimed to determine the prevalence of hearing impairment in Bangladeshi people of all ages. METHODS: A nationally representative cross-sectional survey was carried out in 2013. A total of 4260 subjects (1774 males and 2486 females), with a mean age of 32 years, participated. Hearing impairment was determined by pure tone audiometry and otoacoustic emissions testing. RESULTS: Disabling hearing loss (greater than 40 dB loss in adults, and greater than 30 dB loss in children younger than 15 years, in their better hearing ears) was present in 9.6 per cent (95 per cent confidence interval, 8.5-10.8 per cent) of the respondents. Hearing loss was more prevalent in socio-economically deprived people and in those older than 60 years. Multiple logistic regression analysis identified age, socio-economic deprivation, family history, impacted ear wax, chronic suppurative otitis media, otitis media with effusion, and otitis externa as the significant predictors of disabling hearing loss. CONCLUSION: Deafness prevention should focus mainly on chronic suppurative otitis media, otitis media with effusion, and impacted ear wax prevention, integrated within the primary healthcare system and addressing the equity issue.
