Convolutional Neural Networks (CNNs) have become a cornerstone of computer vision technology, powering a wide array of applications from image classification to facial recognition. However, they are not without their challenges. One significant issue is the requirement for large datasets. CNNs need vast amounts of labeled image data to learn effectively, which can be difficult to obtain, especially in specialized fields like medical imaging. This need for extensive data can also lead to overfitting, where the network performs well on training data but poorly on new, unseen data.
Another challenge is the computational cost associated with training CNNs. The process demands substantial computing power and resources, often necessitating the use of specialized hardware like GPUs or cloud computing resources. This can be a barrier for smaller organizations or individuals who lack access to such infrastructure.
CNNs also struggle with interpretability. The decision-making process within these networks is often seen as a "black box," making it difficult for researchers to understand how specific outputs are generated. This lack of transparency can be problematic, especially in critical applications like healthcare, where understanding the reasoning behind a decision is essential.
Moreover, CNNs are sensitive to variations in input data. Slight changes in lighting, angle, or scale can significantly impact their performance, leading to unreliable results in real-world scenarios. This sensitivity necessitates careful preprocessing