For the image classification problem, which of the following neural networks is more suitable to solve this problem?

Question

Options

• Asensor
• BRecurrent neural network
• CConvolutional Neural Network
• DFully connected neural network

• A
  9% (4)
• B
  4% (2)
• C
  85% (39)
• D
  2% (1)

Explanation

Convolutional Neural Networks (CNNs) are purpose-built for image classification because they use convolutional layers to automatically detect spatial features like edges, textures, and shapes - preserving the 2D structure of images that flat networks destroy. A sensor (option A) is a hardware device, not a neural network architecture at all. Recurrent Neural Networks (option B) are designed for sequential data like text or time series, where order matters - images don't have that kind of temporal dependency. A Fully connected network (option D) can technically learn image features but treats every pixel independently, losing all spatial relationships and becoming computationally impractical for even modest image sizes.

Memory tip: Think "C for Convolutional, C for Camera" - CNNs see images the way a camera captures them, in structured grids of pixels where neighboring values are related.

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