Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS)
Rama Raju, Vetukuri Venkata Siva
Unknown Affiliation
Computer Science & IT Engineering Health Professions

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A novel model to detect and categorize objects from images by using a hybrid machine learning model Sethi, Nilambar; Rama Raju, Vetukuri Venkata Siva; Lokavarapu, Venkata Srinivas; Devareddi, Ravi Babu; Reddy, Shiva Shankar; Nrusimhadri, Silpa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp667-679

Abstract

As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Evaluation of deep learning models for melanoma image classification Reddy, Shiva Shankar; Rama Raju, Vetukuri Venkata Siva; Swaroop, Chigurupati Ravi; Pilli, Neelima
International Journal of Public Health Science (IJPHS) Vol 12, No 3: September 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i3.22983

Abstract

Melanin-producing cells are the origin of melanoma, the worst form of skin cancer (Melanocytes). If this cancer is not caught early, it might spread to other organs. With automated diagnostic technologies, clinicians and non- professionals may better diagnose diseases. Dermoscopic analysis, biopsy, and histological tests may be needed starting with a clinical assessment. Photo-based skin lesion categorization is challenging due to the fine-grained variability of skin lesions. We provide a more reliable melanoma detection model for each suspicious lesion in this paper. A set of characteristics characterizing a skin lesion's borders, texture, and coloursis used to educate convolutional neural networks. The deep learning models were generated using a standard dataset. To know the model's performance, consider the metrics like accuracy, sensitivity, specificity, Jaccard index and Dice coefficient. Transfer learning is used to categorize normal and diseased skin pictures automatically. This model-driven design helps doctors swiftly assess lesions.
Co-Authors Devareddi, Ravi Babu Lokavarapu, Venkata Srinivas Nrusimhadri, Silpa Pilli, Neelima Reddy, Shiva Shankar Sethi, Nilambar Swaroop, Chigurupati Ravi