Proper classification of documents is of tremendous importance for an organization. As digital copies of documents are now available due to technological advances, it has become convenient to classify them automatically using machine learning or deep learning algorithms. Deep CNNs have been widely applied for document image classification, whereas newly introduced transformer-based models have also presented favorable outcomes. In this paper, one of the advanced and very highperforming deep CNN models, ConvNext V2, developed very recently, has been adapted for document image classification task, that leverages the imitation of the self-attention mechanism of transformers through the use of masked autoencoders. Prior studies have suggested that models pre-trained on ImageNet may not perform optimally when directly applied to document classification tasks. Nevertheless, our findings reveal substantial enhancements in performance using ConvNext V2, indicating that further domain-specific pre-training (for instance, on the RVL-CDIP dataset) might not be essential for attaining high accuracy. Our results demonstrate that effective, direct application of ImageNet pre-trained models can yield significant benefits. The model has been applied to one of the most standard document image classification datasets Tobacco-3482. The results show a high overall accuracy of 92.25% with fast convergence, outperforming several other state-of-the-art methods. The source code for this work can be accessed by using the following link: https: //github.com/MdSaifulIslamSajol/document-tobacco-convnextv2

Skin cancer has always been one of the most common types of cancer in medical history. Every year, it kills millions of people around the world. To find skin problems early, it is necessary to have a reliable automated method for recognizing them. In the past, protein sequences and different types of imaging methods were used with machine learning to find skin cancer. The problem with machine learning methods is that they need features to be designed by humans, which is hard to do and takes a lot of time. Image processing and deep learning are both used in the method for treating skin cancer that works. Our study is based on The HAM10000 dataset, which is made up of 10015 different dermatoscopic pictures of common pigmented skin lesions from different sources. The newest deep learning techniques, such as Convolutional Neural Networks (CNN), Transformers, and Hybrid models, i.e., ConvNeXt V2, Swinv2, ViT2, Cvt, DenseNet, RestNet, PVTv2, EfficientNet, EfficientFormer, VGG, MobileNet, MobileViTV2, GoogLeNet, are all compared in this study to see which one is better for automatically detecting skin cancer lesions. We found the best model for identifying skin lesions by determining its accuracy, F1-score, Inference time, and confusion matrix. Among our implemented models, the ConvNeXt V2 model has an accuracy of 93.2% with the lowest inference time. With this research’s help, new ways are being made to find skin cancer, which can lead to better patient results and better clinical decisions.

Brain tumor is a serious disease that can lead to fatal consequences. Moreover, there are different types of brain tumor with different progression rate and severeness. Thus, brain tumor classification is an essential task in medical diagnosis. Convolutional Neural Network (CNN) based deep learning methods shown great potential brain tumor classification using Magnetic Resonance Imaging (MRI) scans of brain. Recent development in transformer based deep learning models have shown promising outcome in image classification task. However, these models have not yet much explored in the application of brain tumor classification. In this paper, we employ seven advanced models which utilizes transformers or mimics the selfattention operation of transformers through their design, for brain tumor classification. To assess their performance for this particular task, five conventional CNN based methods have also been applied and compared with these models. To make our assessment more coherent and comprehensive we perform the comparison for four different datasets. The results indicate that transformer-based advanced models do not provide a distinct advantage over conventional CNN-based models. The CNN-based ResNet-50 performs well overall, especially with smaller datasets. For larger datasets, transformer-based models generally perform better than CNN-based models, although the difference is not significant.