Brain disorders become more complex for individuals due to their neurological and psychological conditions. Brain disorders can be detected using various medical imaging datasets for early and accurate diagnosis, leading to effective treatment. Many existing models accurately detect affected neurological conditions and exhibit various misclassification outcomes depending on the abnormal detection rate. In this context, Adaptive Convolutional Neural Networks (ACNNs) can handle complex and large images for accurate detection and classification. In this paper, the proposed approach combines adaptive CNNs and Capsule Networks (CapsNets) to address issues in 3D medical imaging and brain disorders detection, such as brain tumors and Parkinson's disease. The proposed system was applied to one benchmark brain disorders and accurately detected the abnormal conditions. Particularly, the 3D-CNN layers extract the spatial features at various levels from high-quality MRI images. The Capsule Network enhances the features that represent the relationships among brain disorders and identifies complex patterns in the input MRI images. Experimental results achieved high accuracy of 0.99% for brain tumor detection and classification. These results indicate that the proposed approach has more potential for accurately identifying diseases.
Daily molecular rhythms modulate skin physiology. However, the effects of chronic sunlight exposure on these rhythms remain unstudied. Twenty women aged 50–65 years who exhibited moderate-to-severe photoageing of the dorsal forearm were recruited. Skin biopsies (3 mm) were taken from upper buttock and dorsal forearm of each individual at noon, 18.00 h, 00.00 h, and 06.00 h, across one 24-h cycle. Skin biopsies were analysed by RNA sequencing. Cosinor analysis was used to identify cycling genes along with their amplitudes and peak expression phases. Nested models identified significant differences between photoprotected and photoexposed samples. Phase set and gene set enrichment analyses identified pathways under circadian control. In photoprotected buttock skin 1546 (12%) genes met criteria for cycling. In photoexposed forearm skin the number was reduced to 959 (8%). Overall, 1076 transcripts cycled exclusively in the buttock and 489 exclusively in the forearm. The peak expression times for individual cycling transcripts were clustered in the early morning and mid-afternoon. Focusing on transcripts that cycled in both sites revealed that cycling in the buttock was of overall higher amplitude (P < 2.2e−16). For these transcripts, distributions of peak times were significantly different between forearm and buttock skin (P < 0.001), with peak times advanced in forearm skin. Genes involved in the unfolded protein response, DNA repair, and Myc targets were enriched among those that cycled exclusively in buttock skin. Inflammatory response and epithelial–mesenchymal transition pathway genes were enriched among those that cycled exclusively in forearm skin. Tumour necrosis factor-α signalling pathway genes and Myc targets were also enriched among genes that cycled in both skin sites. Altered cycling patterns and a reduced number of cycling genes in photoexposed compared with photoprotected skin, suggest that chronic UV exposure may reprogram circadian output rhythms in anticipation of daily environmental stressors, which may in turn compromise the optimal functioning of other biological processes.
Underwater image improvement is a complex task that is affected by light occlusion, scattering, and wavelength distortion in aquatic environments. In this paper, an Ensemble Deep Map-Learning System (EDMLS) is presented that increases the quality of real-time underwater images. The proposed EDMLS uses white balancing, histogram equalization, and dehazing filters, along with a deep convolutional enhancement system that refines color mapping, texture details, and illumination balance. The proposed system first refines the physical fluctuations using specialized techniques and integrates them with convolutional layers, resulting in high image quality. The final layers of the proposed approach include residual learning and attentionbased fusion layers to improve spatial color features, thereby mitigating the effects of traditional colors. Experiments are conducted on benchmark datasets, yielding significant results. The proposed approach achieves high performance across several metrics compared with existing models.
