CNN and Transfer Learning Methods for Enhanced Dermatological Disease Detection

Since skin diseases generally badly affect lives, the earlier and more accurate the diagnosis, the better the chances of effective treatment and a better prognosis. Deep learning applications, especially CNNs, has revolutionized the domain of disease classification, significantly increasing the accuracy of diagnoses for such common conditions and facilitating early interventions. The huge success behind the ongoing project motivated advancements of the developing in CNN techniques towards detection of skin disease by using the concept of Transfer Learning. So, the older models, which had employed it for detecting Eczema and Psoriasis based on the architectures involving deep CNNs. The Inception ResNet v2 architecture improved the accuracy of that model, with some practical implementations via smartphone integration and web server integration. Some of those innovations are as follows in our project. The earlier work used different CNN architectures. Our approach involved Transfer Learning with a pre-trained ResNet50 model to try to improve performance and efficiency using features learned from large-scale datasets. This reduce the complexity and enhance the accuracy. Besides Transfer Learning adaptation, our project encompasses elaborate preprocessing techniques like resizing, normalization, and data augmentation in fine-tuning the dataset for further model fine-tuning. It has 97.6% accuracy, 95% precision, 99.4% recall, and 97.4% F1-score. rad-CAM techniques have been employed to visualize and interpret model predictions. This final model has been a pragmatic and accessible tool for early detection and diagnosis of skin disease. The feature here is an attempt to provide a more accurate, efficient, and user-friendly diagnostic solution through the incorporation of advanced methods of Transfer Learning and visualization

Spectroscopy and near infrared upconversion of Er³⁺-doped TZNT glasses

In this paper we report on the near infrared (NIR) upconversion (UC) and spectroscopic properties of erbium (Er3+)-doped TeO2-ZnO-Nb2O5-TiO2 (TZNT) oxide glasses. Judd-Ofelt theory has been applied to investigate the intensity parameters (Ωλ, λ=2, 4 and 6) which are used to derive radiative properties of the fluorescent levels. The different glasses present high refractive indices, low dispersion and Abbe numbers, as determined by variable angle spectroscopic ellipsometry. Under 980 nm excitation, the NIR emission profile and full width at half maximum have been studied in a broad range of Er3+ concentrations (0.01-3.0 mol%). On the other side, NIR UC has been obtained by exciting at 1523 nm, showing an increase of the intensity with Er3+ ion density in the studied range. The decay curves of the 4I13/2 level exhibit single exponential nature for all the different concentrations. The lifetime of the 4I13/2 level has been found to decrease (3.73-1.20 ms) after an initial increase (3.65-3.73 ms) with increasing of Er3+ ion concentration. The TZNT samples show broadband UC emission at 1.0 μm, which match with the band gap of silicon. This reveals that the investigated glasses could find application in photonics, for example non-linear optics and photovoltaic's.</p

CNN and Transfer Learning methods for enhanced dermatological disease detection

Since skin diseases generally badly affect lives, the earlier and more accurate the diagnosis, the better the chances of effective treatment and a better prognosis. Deep learning applications, especially CNNs, has revolutionized the domain of disease classification, significantly increasing the accuracy of diagnoses for such common conditions and facilitating early interventions. The huge success behind the ongoing project motivated advancements of the developing in CNN techniques towards detection of skin disease by using the concept of Transfer Learning. So, the older models, which had employed it for detecting Eczema and Psoriasis based on the architectures involving deep CNNs. The Inception ResNet v2 architecture improved the accuracy of that model, with some practical implementations via smartphone integration and web server integration. Some of those innovations are as follows in our project. The earlier work used different CNN architectures. Our approach involved Transfer Learning with a pre-trained ResNet50 model to try to improve performance and efficiency using features learned from large-scale datasets. This reduce the complexity and enhance the accuracy. Besides Transfer Learning adaptation, our project encompasses elaborate preprocessing techniques like resizing, normalization, and data augmentation in fine-tuning the dataset for further model fine-tuning. It has 97.6% accuracy, 95% precision, 99.4% recall, and 97.4% F1-score. rad-CAM techniques have been employed to visualize and interpret model predictions. This final model has been a pragmatic and accessible tool for early detection and diagnosis of skin disease. The feature here is an attempt to provide a more accurate, efficient, and user-friendly diagnostic solution through the incorporation of advanced methods of Transfer Learnin3g and visualization

Harmful and beneficial aspects of Parthenium hysterophorus: an update

Parthenium hysterophorus is a noxious weed in America, Asia, Africa and Australia. This weed is considered to be a cause of allergic respiratory problems, contact dermatitis, mutagenicity in human and livestock. Crop production is drastically reduced owing to its allelopathy. Also aggressive dominance of this weed threatens biodiversity. Eradication of P. hysterophorus by burning, chemical herbicides, eucalyptus oil and biological control by leaf-feeding beetle, stem-galling moth, stem-boring weevil and fungi have been carried out with variable degrees of success. Recently many innovative uses of this hitherto notorious plant have been discovered. Parthenium hysterophorus confers many health benefits, viz remedy for skin inflammation, rheumatic pain, diarrhoea, urinary tract infections, dysentery, malaria and neuralgia. Its prospect as nano-medicine is being carried out with some preliminary success so far. Removal of heavy metals and dye from the environment, eradication of aquatic weeds, use as substrate for commercial enzyme production, additives in cattle manure for biogas production, as biopesticide, as green manure and compost are to name a few of some other potentials. The active compounds responsible for hazardous properties have been summarized. The aim of this review article is to explore the problem P. hysterophorus poses as a weed, the effective control measures that can be implemented as well as to unravel the latent beneficial prospects of this weed