No abstract

Considering the frightening high level of mortality from cancer, studies of anticancer agents are vital nowadays. The 24 thioderivatives of 2‐alkyl(aryl)‐quinazolin‐4(3H)‐thiones and 20 thioderivatives of [1,2,4]triazolo[1,5‐c]quinazoline‐2‐thiones were synthesized and evaluated for preliminary in vitro anticancer activity with subsequent in silico QSAR analysis. The substance 18 had the best results inhibiting growth of eight cancer cell lines: CCRF‐CEM of leukemia; SF‐539, SNB‐75, and U251 of CNS cancer; 786, RXF393, and UO‐31 of renal cancer; and MDA‐MB‐231/ATCC of breast cancer (−31.50 – 47.41% of cell growth) with low procancer effect. Calculated QSAR‐models for CCRF‐CEM of leukemia, T‐47D and HS 578T of breast cancer, and mean cell growth demonstrated good rate of anticancer activity prediction (r2 = 0.7 – 0.8, QnormalLOO2 = 0.5 – 0.7).

In order to classify skin lesions, many efforts have been made to create various automated diagnostic systems. For that purpose many efforts have been put in creating various automated diagnostics systems Nowadays, with the rapid advancements in deep learning, Vision Transformers have emerged as powerful models for image processing and analysis purposes. This type of model has already proved useful for cancer detection and classification tasks in particular. However, the complexity and variability of skin lesions present significant challenges in accurately classifying them. Integrating the concept of fractal dimension into Vision Transformers can potentially improve their performance by capturing the intricate structural patterns of skin lesions. This paper aims to explore the integration of fractal dimension metrics into a Vision Transformer for skin cancer classification. The problem at hand is to investigate the integration of fractal dimension metrics into the existing Vision Transformer architecture for the accurate classification of skin lesions as cancerous or non-cancerous. Fractal dimensions provide a measure of the complexity and irregularity of an object, which can be informative in characterizing skin cancer lesions. We aim to research possability and ways of incorporating fractal dimension metrics into the Vision Transformer model for results improvements.

This paper investigates the use of vision transformers (ViT) for skin cancer classification tasks, compared to convolutional models. We propose a novel ViT architecture that effectively classifies skin cancer images. Our findings suggest that ViT models have the potential to outperform convolutional models, especially with larger datasets.