In this paper, we have tried to evaluate the chance of deep learning algorithm namely Convolutional Neural Network (CNN) to detect skin cancer classifying benign and malignant mole.
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skin cancer ppt.pptx
1. AI BASED ANALYSIS OF SKIN CANCER
IMAGES USING ORANGE TOOL
AUTHORS:
MAANEESHA S (STUDENT)
SIVALAKSHMI P(ASSOCIATE
PROFESSOR)
R.M.K. COLLEGE OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF ELECTRONICS AND COMMUNICATION
ENGINEERING
2. OBJECTIVE
We are going to perform
classification of skin lesions
using deep learning.
Based on the classification we
can predict weather cancer is
present or not.
We are using Convolutional
Neural Network , Logistic
regression and naรฏve bayes
to classify and train the
model.
3. ABSTRACT
โข Skin cancer is one of the major health challenges that have claimed the lives of
many, melanomas type skin cancer contributes more every day.
โข According to American statistics, every day 5 Nearly 20 Americans die from
melanoma. In 2021, it is estimated that 7,180 deaths will be attributed to
melanoma โ 4,600 men and 2,580 women and the survival rate can be increased
by early detection.
โข An automated deep learning algorithm enables the early diagnosis of benign and
malignant lesions in the skin, which could lead to low-cost and early disease
diagnosis.
4. ๏ถ This design proposes a system using an end-to-end deep literacy
frame for an effective skin cancer recognition system grounded on a
convolutional neural network (CNN) for a concerted prize of the
features and performs recognition and to classify dermal cell images
and to detect skin cancer.
๏ถ Thus, this design has been proposed for enhancing the recognition
rate with lower time and more effectiveness and with a smaller
number of duplications needed by using CNN.
๏ถ The proposed deep learning models built here are tested on standard
datasets, and the metric area under the curve of 83.13% accuracy
was observed
PROPOSED SYSTEM