Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.

The approach of image processing and deep learning has shown to be a breakthrough in the field of medical image diagnosis such as dermoscopic image analysis for skin cancer recognition and their classification. Skin cancer cases are increasing every year and pose a significant threat for health. In recent studies, convolutional neural network (CNN) has accomplished remarkable success in classifying skin cancer images. CNN is limited to extracting features from minor objects from input dermoscopic image and fails to pinpoint significant regions. Consequently, the researchers of this study have utilized vision transformers (VIT), known for their robust performance in conventional classification assignments. The self-attention mechanism (SAM) aims to enhance the significance of pivotal characteristics while modifying the influence of noise-inducing features. Specifically, an enhanced transformer network architecture has been introduced in this context. To assess its effectiveness, several enhancements have been applied to the model. Initially, a ViT network is implemented to evaluate its efficacy in identifying skin cancer. Subsequently, Adaptive-scale image fragmentation is utilized to sequentially process the image, emphasizing adaptive-scale features through patch embedding. Furthermore, contrastive learning is employed to ensure that similar skin cancer data is encoded differently, aiming for distinct encoding outcomes for different data. Skin cancer dataset namely ISIC 2019 is retrieved in this study, locally accessible at Kaggle’s official website. This dataset consists of dermoscopic images of skin cancer having several types: dermatofibroma, melanoma, actinic keratosis, basal cell carcinoma, nevus, vascular lesion, and pigmented benign keratosis. The ViT model has achieved 99.66% accuracy, 94.85% precision, 93.74% recall, and 94.52% f1-score. Three deep learning models Inception V3, MobileNet, and ResNet-50 were also applied with transfer learning approach as comparison to proposed ViT model for performance evaluation that resulted in 72%, 94.3, and 89% accuracies, respectively. The transformer network has shown remarkable success in natural language processing and in the domain of image analysis. These achievements establish a solid groundwork to classify skin cancer using multimodal data. This paper is confident to captivate the attention of medical researchers, computer engineers, dermatologists, and scholars across various related disciplines. Its insights promise to offer enhanced convenience for patients in their respective fields.