1. Definition and Basics:

   • ANNs are computational models inspired by the human brain’s neural network. They consist of interconnected artificial neurons (also called nodes or units) that process information.

   • Each neuron receives input, performs a weighted sum, applies an activation function, and produces an output.

   • The architecture of ANNs can be shallow (with one hidden layer) or deep (multiple hidden layers).

2. Types of ANNs:

   • Feedforward Neural Networks (FNNs): The most common type. Information flows in one direction, from input to output.

   • Recurrent Neural Networks (RNNs): Suitable for sequential data (e.g., time series). They have loops that allow feedback connections.

   • Convolutional Neural Networks (CNNs): Designed for image and spatial data. They use convolutional layers to learn hierarchical features.

   • Long Short-Term Memory (LSTM) Networks: A type of RNN that handles long-term dependencies.

3. Architecture:

   • ANNs consist of layers:

     • Input Layer: Receives raw data (features).

     • Hidden Layers: Process information using weights and activation functions.

     • Output Layer: Produces the final prediction or classification.

   • Neurons within layers are interconnected by weights.

   • Training involves adjusting weights using backpropagation and gradient descent.

4. Why Use ANNs?:

   • ANNs excel at:

     • Non-linearity: Capturing complex relationships in data.

     • Feature Learning: Automatically learning relevant features.

     • Generalization: Making accurate predictions on unseen data.

   • They handle structured and unstructured data (e.g., images, text, time series).

5. Medical and Biomedical Applications:

   • Disease Diagnosis and Prognosis:

     • ANNs analyze patient data (symptoms, lab results, medical images) to predict diseases (e.g., cancer, diabetes).

   • Medical Imaging:

     • Deep Convolutional Neural Networks (DCNNs) are widely used for:

       • Segmentation: Identifying regions of interest (e.g., tumors) in medical images.

       • Classification: Diagnosing diseases based on X-rays, MRIs, CT scans.

   • Drug Discovery and Genomics:

     • ANNs predict drug interactions, identify potential drug candidates, and analyze genomic data.

   • Healthcare Management:

     • ANNs optimize hospital resource allocation, predict patient outcomes, and improve patient care.

In summary, ANNs play a crucial role in medical research by enabling accurate disease prediction, medical image analysis, and personalized treatment. Researchers and medical professionals continue to explore their potential for improving healthcare outcomes