Skin Cancer Detection using Deep Learning Matlab

 

Abstract

Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.

1. Introduction

Skin cancer is one of the most common types of cancer that begins with the uncontrolled reproduction of skin cells. It can occur because of the ultraviolet radiation from sunshine or tanning beds, and it causes skin cells to multiply and form malignant tumors.

Skin cancer is one of the primary reasons for deaths worldwide. According to statistics published by , 97,160 Americans were diagnosed with skin cancer in 2023, which is 5.0% of the total cancer cases reported in the United States, and 7990 people died because of skin cancer which is 1.3% of the total deaths because of skin cancer in the United States. Melanoma is one of the most common and dangerous types of skin cancer that can spread quickly to other body parts. Approximately 21 out of 100,000 melanoma cases were diagnosed in the United States between 2016 and 2020. The death rate because of melanoma was 2.1 per 100,000 diagnosed cases, and 1,413,976 people were living with melanoma in 2020. The five-year survival rate of skin melanoma is 93.5% which is relatively high. The five-year survival rate is 99.6% when skin melanoma is diagnosed at an early stage. There are more chances of survival when skin melanoma is localized, which means it does not spread to other body parts, but only 77.6% of skin melanomas are diagnosed at the local stage. The number of deaths because of skin melanoma can be reduced if it is detected at its early stages.


NOTE:-

Matlabhelpers.com provide latest MatLab Homework Help,MatLab Assignment Help , Finance Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.    

The most common method of diagnosing skin cancer is by visual examinations by dermatologists, which has an accuracy of about 60%. The diagnostic accuracy of skin cancers increases to 89% by using dermoscopy. We also want to diagnose skin cancers with high sensitivity; dermoscopy has a sensitivity of 82.6% for detecting melanocytic lesions, 98.6% for basal cell carcinoma, and 86.5% for squamous cell carcinoma. Dermoscopy increases the accuracy of melanoma diagnosis, but it may still be challenging to diagnose some lesions, particularly early melanomas accurately, that lack distinctive dermoscopic features. Though dermoscopy diagnoses skin melanoma with very good accuracy, it is not well suited for diagnosing featureless melanoma, and there is still a need to improve accuracy further to increase the survival rate of patients. The problems with dermoscopy and the need to increase the diagnostic accuracy of skin cancer further laid the foundation for developing computer-aided detection methods for diagnosing skin cancers.

SEE COMPLETE ANSWER CLICK THE LINK 

Comments

Post a Comment

Popular posts from this blog

Why do I get a "Too many input arguments" error when not passing any?

  I am working on some simple object-oriented code in MATLAB. I am trying to call one of my class methods with no input or output arguments in its definition. Function definition: function roll_dice Function call: obj.roll_dice; When this is executed, MATLAB says:    ??? Error using ==> roll_dice Too many input arguments. Error in ==> DiceSet>Diceset.Diceset at 11 obj.roll_dice; (etc...) Anyone have any ideas what could be causing it? Are there secret automatic arguments I'm unaware that I'm passing?   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.     Answers:  When you ma...
Read more

How is full convolution performed using MATLAB's conv2 function?

  Answer In MATLAB, the  conv2  function is used to perform 2D convolution between two matrices. The syntax of  conv2  is as follows:    C = conv2(A, B) Here,  A  and  B  are input matrices, and  C  is the output matrix after convolution. When  A  and  B  have the same size, the convolution is called "full convolution," which means that the output matrix  C  has the same size as the sum of the sizes of  A  and  B  minus 1. To perform full convolution using  conv2 , the input matrices  A  and  B  are first flipped both horizontally and vertically. Then, the flipped  B  matrix is slid over  A  matrix, and the dot product of the overlapped elements is computed and added up. This process is repeated for all possible positions of  B  on  A . The resulting matrix is the full convolution output  C . Here is an exampl...
Read more

Why Red, Green, Blue channels of image separetely are grayscaled (Matlab)?

  Answer When an image is loaded into MATLAB, it is usually stored as a matrix of values representing the intensities of the pixels in the image. For a color image, this matrix is usually three-dimensional, with one dimension for each color channel (red, green, and blue). To convert a color image to grayscale, one common approach is to take a weighted average of the red, green, and blue channels, using coefficients that reflect the relative importance of each color channel in human perception. In MATLAB, the  rgb2gray  function uses the formula  0.2989 * R + 0.5870 * G + 0.1140 * B  to convert a color image to grayscale, where R, G, and B are the red, green, and blue channels of the image, respectively. These coefficients NOTE:- Matlabhelpers.com  provide latest  MatLab Homework Help , MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil wit...
Read more