Recognition: Advancing Image Classification Using DCNN Algorithm

The primary goal of this study is to enhance the accuracy of fish species detection using the DCNN algorithm. However, to provide a deeper understanding of seafood, it is necessary for the general public to comprehend the various types of fish. Previous research has focused on classifying fish based on behavior, farming and other factors in underwater environments. The major challenges in fish classification include background noise, image distortion, object occlusion, image quality, and the presence of other objects. Identifying the type of fish is crucial in daily life, particularly for food supply purposes. This study comprises two sections. The first section involves image preprocessing and segmentation, which are fundamental components used to reduce noise and enhance the images. In the second section, we extract relevant information using grad cam visualization and the DCNN algorithm to identify fish categories. Compared to previous approaches, this strategy significantly improves discrimination accuracy. Overall, the study employs a highly professional approach to address the challenges associated with fish classification and enhance the accuracy of fish species detection.