In histology, the classification process using k-NN involves several steps:
Data Collection: Gather histological images and label them according to their tissue type or pathology. Feature Extraction: Extract relevant features from these images such as texture, shape, and color. Distance Metric: Choose an appropriate distance metric (e.g., Euclidean, Manhattan) to measure the similarity between images. Classification: Use the k-NN algorithm to classify the new histological image based on the majority class of its 'k' nearest neighbors.
