Felis catus disease detection in the digital era: Combining CNN and Random Forest

Cat skin illnesses have traditionally been diagnosed manually, which can be laborious and arbitrary. Researchers offer a novel solution to this problem that utilizes Max Pooling layers and neural networks based on convolution (CNNs) for autonomous disease recognition. To extract complex features while boosting generalization, the study focuses on combining two convolutional layers into a CNN architecture, before adding a Max Pooling layer. By combining the effectiveness of Max Pooling and CNNs, this integration accelerates processing while learning significant texture and pattern information from photos. They show the efficacy of the method in correctly detecting a wide range of cat skin illnesses through careful experimentation and performance evaluation. Utilizing a selected dataset of medical photos, data enhancement techniques, and several measures like precision, recall, precision, and F1-score, they test the efficacy of our methodology. The findings demonstrate the potential of combining Max Pooling and convolutional feature extraction for automating and enhancing the accuracy of cat skin disease diagnosis. This study offers a strong tool that improves efficiency and accuracy and advances veterinary dermatology diagnosis. The suggested method follows the current state of digital diagnostics and holds out the prospect of an entirely novel age of streamlined identification of illnesses for feline health and veterinary care.