MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202541005964 A) filed by Dr S. Brindha on January 24, 2025, for "automated Dermo Assistant".
Inventors include Dr S. Brindha; D. Priya; K. Sudha; K. Thamaraiselvi; A. J. Gaviya; B. Nithika; Preetha Raj; R. Srinithi; P. Gokul Nath; C. Gopi Krishna; K. Jothiraj; H. Mohammed Dhanish; and G. Suriya Krishna.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: ABSTRACT OF THE INVENTION AUTOMATED DERMO ASSISTANT Eczema is the most common among all types of skin diseases a solution for this disease is very crucial for manually by doctors or dermatologists early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can be costly and time - consuming. Many methods such as Image Processing Techniques , Machine Learning algorithms are getting used to execute segmentation and classifi~atiun of skin diseases . There is also insufficiency in eczema disease dataset . Many methods such as Image Processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin disease . It is found that among all those skin disease detection systems, particularly detection work on eczema disease diseases is rare In this paper, an prediction eczema model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature ,we propose a novel deep CNN-based approach for classifying five different classes of eczema with our collected dataset In addition, it sometimes leads to skin cancer in severe cases. Subsequently, diagnosing skin diseases from clinical images is one of the foremost challenging tasks in medical image analysis. Moreover, when performed manually by medical experts, diagnosing skin diseases is time-intensive and subjective. As a result, both patients and dermatologists require automatic skin disease prediction, which makes the treatments plan faster. Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. CNN-based approach for classifying six different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. The proposed CNN. architecture includes multiple convolutional layers for feature extraction, followed by pooling and dense layers for classification. This project presents an Al-powered image processing system for the prediction of eczema skin diseases, leveraging Convolutional Neural Networks (CNNs) for automated diagnosis. The system is designed to analyze highresolution skin images, extract essential features, and classify different eczema types with
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