A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm.

• The proposed strategy improves skin cancer diagnosis and patient outcomes. • Gated Recurrent Unit Network optimized by Orca predation Algorithm is used as a tool. • The tool is modified by an improved version of Orca predation Algorithm. • The tool shows an admirable result compared with some other techniques. • The method improves skin cancer diagnostics, enabling more accurate and efficient methods. Skin cancer is a risky ailment that can be effectively managed if detected promptly. However, timely recognition of skin cancer remains a challenge. In this study, a robotic computer-assisted tactic is proposed for the early detection of skin cancer. The motivation behind this research is to improve the accuracy and efficiency of skin cancer recognition. The proposed methodology involves a series of steps. Initially, the input images undergo preprocessing to enhance their quality and extract relevant features. These preprocessed images are then fed into a Gated Recurrent Unit (GRU) Network, a type of deep learning model known for its ability to capture sequential information. To optimize the performance of the GRU Network, we employ an enhanced variant of the Orca Predation Algorithm (OPA). This algorithm helps fine-tune the network parameters, improving its diagnostic capabilities. To validate and evaluate the effectiveness of our skin cancer diagnosis algorithm, we conducted experiments using the HAM10000 dataset, which contains a large collection of skin lesion images. We compared the results obtained from our proposed method, named GRU/IOPA, with eight existing techniques commonly used for skin cancer diagnosis. Five execution indices, namely sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were used to assess the performance of these methods. Our findings demonstrate that the GRU/IOPA system outperforms other existing methods in terms of sensitivity (0.95), specificity (0.97), PPV (0.95), NPV (0.96), and accuracy. These results indicate the effectiveness of our proposed method in diagnosing skin cancer compared to traditional approaches. The superior performance of GRU/IOPA highlights its potential impact on improving skin cancer diagnosis and reinforces its promise as an advanced tool in the field. In conclusion, our study presents a novel approach for timely skin cancer recognition through a robotic computer-assisted tactic. By utilizing the GRU/IOPA system, we achieve superior accuracy and efficiency in diagnosing skin cancer compared to existing techniques. This research offers significant contributions to the field of skin cancer diagnosis and opens up new avenues for future advancements in this area. [ABSTRACT FROM AUTHOR]