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A Convolutional Neural Network (CNN) is a type of deep learning algorithm specifically designed for image processing and recognition tasks. Compared to alternative classification models, CNNs require less preprocessing as they can automatically learn hierarchical feature representations from raw input images. They excel at assigning importance ...
A guide to understanding CNNs, their impact on image analysis, and some key strategies to combat overfitting for robust CNN vs deep learning applications.
- What Is CNN?
- How Does Convolutional Neural Network Work?
- Business Applications of Convolutional Neural Networks
- Predictive Analytics — Precision Medicine
- Conclusion
Convolutional Neural Networkis a type of artificial deep learning neural network primarily used in a variety of computer vision/image recognition operations. This process includes the following operations: 1. Image recognition and OCR 2. Object detection for self-driving cars 3. Face recognition on social media 4. Image analysis in healthcare The t...
Convolutional Neural Network architecture consists of four layers: 1. Convolutional layer — where the action starts. The convolutional layer is designed to identify the features of an image. Usually, it goes from the general (i.e., shapes) to specific (i.e., identifying elements of an object, the face of certain man, etc.). 2. Then goes Rectified L...
Image Classification — Search Engines, Recommender Systems, Social Media
Image recognition and classification in its various forms is the primary field of use for convolutional neural networks. It is also the one use case that involves the most progressive frameworks (especially, in the case of medical imaging). The purpose of the CNN image classification is the following: 1. Deconstruct an image and identify its distinct feature (the job of supervised machine learning classification algorithm) 2. Reduce the description to its key credentials (this is the job of d...
Face Recognition — Social Media, Identification procedures, Surveillance
Face recognition deserves a separate mention. It is a subdivision of image recognition dedicated to the comprehension of more complex types of images — human faces (or other living beings, animals, fish, and insects included). The difference between straight image recognition and face recognition is in the complexity of the operation. There is an additional layer of work involved. 1. First goes basic object recognition — the shape of the face and its features are recognized. 2. Then the featu...
Legal, Banking, Insurance, Document digitization — Optical Character Recognition
Optical Character Recognition, aka OCR, is a variation of image recognition specifically designed to process written and print symbols, graphs, and charts. Just like face recognition, it involves a more complicated process with move moving parts. In its core, OCR is a combination of computer vision with natural language processing. First, the image is recognized and deconstructed into characters; then, the characters are extracted together into a coherent whole. Here’s how it works: 1. First...
The similar approach also can be used with the existing drugs during the development of a treatment plan for patients.Precision medicineis the emerging subdivision of health risk assessment specifically designed to combine available medical resources and the state of the patient and determine the most effective way of treating the disease. Precisio...
Convolutional Neural Networks have the potential of uncovering and describing the hidden and apparent data in an accessible and insightful manner. Even in its most basic applications, it is impressive how much is possible with the assistance of a neural network. The way CNN recognize images says a lot about the composition and execution of the visu...
- Volodymyr Bilyk
- Image Classification – Search Engines, Recommender Systems, Social Media. Image recognition and classification is the primary field of convolutional neural networks use.
- Face Recognition Applications of RNN is Social Media, Identification procedures, Surveillance. Face recognition deserves a separate mention. This subdivision of image recognition comprehends more complex images.
- Legal, Banking, Insurance, Document digitization – Optical Character Recognition. Optical Character Recognition was designed for written and print symbol processing.
- Medical Image Computing – Healthcare Data Science / Predictive Analytics. Healthcare is the industry where all the cutting edge technologies get their trial on fire.
Feb 2, 2024 · In this article, we turn our focus towards the real-world applications of CNNs, demonstrating how this technology extends beyond theoretical and laboratory settings to become a powerful tool transforming our daily lives and various industries. CNNs find application in a wide array of fields, from enhancing the capabilities of image and video recognition to pioneering innovations in medical ...
Feb 7, 2024 · Convolutional Neural Networks, commonly referred to as CNNs are a specialized type of neural network designed to process and classify images.
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Dec 15, 2018 · A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand ...
