Leveraging deep learning algorithms to predict an object or its specific features from an image is a problem which has multiple applications. Such problems are called Image classification problems. As such, image classification is a process of labelling groups or pixels according to predefined rules based on different characteristics of an image.
Additionally, the problem of determining how a deep learning model perceives various intricacies of an image to base a prediction is vital for faster insights/actions and deep-dive analysis. For such model predictions to be trusted, it is important that not only the predictions are on par with human prediction but also that it must be trained to deduce the same root causes as a human would observe.
Business Use Cases and Applications
There are multiple direct and indirect applications of this experiment. Some of them include:
- Healthcare and Medicine – In many healthcare domain specific applications, image classification plays a crucial role in identifying the extent of diseases and understanding underlying patterns in various images like X-Rays, CT Scan, Ultrasound images etc.
- Website Content Moderation – In many sites like YouTube, it is necessary to moderate certain types of content in order to make the content comply with website guidelines. In such cases, Image classification along with explainability as to why particular content needs to be removed helps reduce manual efforts.
- Retail Stores – The cameras installed in retail stores can act as a bridge to identify which aisles are empty and which specific stocks need refilling.
- Banking – The usage of classification models to detect fraud in cheques and other financial statements is manifold especially when the volume of such documents is increasing day by day and it is becoming nearly impossible to manually go through all the documents and validate which ones have a forged signature and which ones do not.
- Manufacturing – In the manufacturing business, early stage fault detection in machines can save a lot of damage. Many machines wear and tear over time and may become faulty delivering defective outcomes. Sometimes the defect is nearly unnoticeable by the human eye. However, a trained model can identify such early stage defects and help to rectify in good time.