Online ISSN: 2515-8260

Keywords : Convolutional Neural Network

Skin Cancer Detection Using VGG-16

Kanneboina Manasa; Dr.G.Vishnu Murthy

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 1, Pages 1419-1426

Skin cancer is a dangerous disease. Benign and malignant melanomas are one of the skin cancer diseases. Melanoma is a highly dangerous disease. It can be curable if it is detected early. Benign can be cured easily but malignant cannot be cured fastly. Benign and malignant melanoma appears in the  early stages while differentiating them. Different methods have been used for differentiating them. Skin cancer can be detected in early stages by visualizing with clinical screening by dermoscopic analysis. Detecting automatically skin lesion is a typical task. Skin cancer symptoms are small blood vessels visible, thickened patch, ulcers and bleed. Skin cancer detected by capturing images with a skin magnifier with polarized light and diagnosed with deep learning classifier in which data augmentation and weights can be added to it. In this CNN classifier is used in which RESNET-50 and VGG-16 were used in which image were resized and weights were added and then the augmentation of the data can be done.


K. Kalyani

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1540-1543
DOI: 10.31838/ejmcm.07.09.167

Mammography is a method for the diagnosis and screening of the human breast using low-energy X-rays. Mammograms tend to detect breast cancer early, usually by detecting standard masses, or by detecting microcalcifications.Adescriptive analysis of mammogram diagnosis using Convolutional Neural Network (CNN)for spectral detection is presented in this study.Initially, the color components are separated as red, green and blue. Only green channel is used for analysis because green is sensitive for humans. Finally,CNN is used for mammogram color spectraldetection. The performance of proposed system is analyzed by CNN in terms of accuracy.

An Empirical Study of Deep Learning Strategies for Spatial Data Mining

K. Sivakumar; A.S. Prakaash

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5124-5132

The emergence of scalable frameworks for machine learning to efficiently analyse and derive valuable insights from these data has triggered growing volumes of data collected. Huge spatial data frameworks cover a wide variety of priorities, including tracking of infectious diseases, simulation of climate change, etc. Conventional mining techniques, especially statistical frameworks to handling these data, are becoming exhausted due to the rise in the number, volume and quality of spatio-temporal data sets. Various machine learning tasks have recently shown efficiency with the development of deep learning methods. We therefore include a detailed survey in this paper on important impacts in the application of deep learning techniques to the mining of spatial data.


Dr.Syed Khasim; Dr. T. Thulasimani; Chandra Sekhar Reddy L; Apurv Verma; Rajakumar M P

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3234-3240

Nowadays, the usage of internet based applications and services are widely used such as
travelling, food ordering, shopping, shipping, etc. In this paper, we propose Deep queue learning
method for predicting and ranking of online food ordering delivery applications. Online website
and mobile applications are available commercially deliver the food and provides variety of
discounts. In this work, we cluster the food and rank based on customer reviews, ordering/delivery
time, user satisfaction and cost. Ranking is done by using Association rule mining for food items
placing, repetitive orders and making places. The objective behind this how this platform is more
useful for customer as well as suppliers. We take opinion poll from customers and suppliers that is
also considered for comparison. The technology are growing rapidly some system is needed for
monitoring online processing and applications. We use Google TensorFlow for analyzing and
predicting the performance of online food ordering and delivery applications. Deep queue learning
model is proposed for applying our input attributes and Python API code for testing accuracy. The
trained and test dataset is collected from various applications. Reviews and opinion is also taken
into account. For these inputs we create deep convolutationl neural network model for making
effective decisions. The results and ranking are calculated by using TensforFlow and performance
is compared.


Gouri Nandan; Dr. Neeba E A

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 5467-5475

Sign languages are languages that solely utilize gestures to convey meaning. Communication, based on the sign language is a mix of manual explanations and non-manual elements. Sign language recognition framework positively reflects communication between the person who is hard of hearing and world around. It also helps in communicating with machines. One of the most utilized types of gesture based communication is the American sign language (ASL). In the proposed work, the letters are detected from a video frame using convolutional neural network (CNN) and then converted into speech using Google Text-to-Speech (gTTS). The systems are trained with 75% of images and tested with 25% of images from the database.


Leelavathy S; Jaichandran R; Shobana R; Vasudevan .; Sreejith S Prasad; Nihad .

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2999-3003

Skin types of diseases are most common among the globe, as people get skin disease due to inheritance, environmental factors. In many cases people ignore the impact of skin disease at the early stage. In the existing system, the skin disease are identified using biopsy process which is analyzed and medicinal prescribed manually by the physicians. To overcome this manual inspection and provide promising results in short period of time, we propose a hybrid approach combining computer vision and machine learning techniques. For this the input images would be microscopic images i.e histopathological from which features like color, shape and texture are extracted and given to convolutional neural network (CNN) for classification and disease identification. Our objective of the project is to detect the type of skin disease easily with accuracy and recommend the best and global medical suggestions.
This paper proposes a skin disease detection method based on image processing and machine learning techniques. The patient provides an image of the infected area of the skin as an input to the prototype. Image processing techniques are performed on this image and feature values are extracted and the classifier model predicts the disease. The proposed system is highly beneficial in rural areas where access to dermatologists are limited. For this proposed system, we use Pycharm based python script for experimental results.