Skin Cancer Detection using Deep Learning and Machine Learning

As the time has passed we have observed that the cases of cancer are increasing day by day. Not a day in a year goes by when a death has not occurred due to the complication of cancer. Skin cancer is one of the most common cancer as it not limited to one gender or particular age, The problem with th

Project Title

Skin Cancer Detection using Deep Learning and Machine Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

As the time has passed we have observed that the cases of cancer are increasing day by day. Not a day in a year goes by when a death has not occurred due to the complication of cancer. Skin cancer is one of the most common cancer as it not limited to one gender or particular age, The problem with the death due to increase in complication is related to the late detection of the cancer or the overall cost for the treatment is expensive. Skin cancers such as basal cell carcinoma, squamous cell carcinoma, and melanoma are becoming more prevalent.

The detection of this cancer is done by many methods but the thing which is common in them is that they are expensive. And many people who might don’t have skin cancer there skin problem can be due to sensitivity or rash or allergy, due to them they delay the process hence making it critical for the patients who needed attention on time.

We can incorporate a way to predict whether the patient is cancerous or not using the machine learning algorithm and Deep Learning Algorithms. Machine learning algorithm has come far beyond from their boundaries and limitations.

Because of their potential pattern recognition capabilities, deep learning (DL) models have gotten a lot of interest in medical imaging. DNNs used for illness diagnosis are methodically focused on increasing prediction accuracy without giving a figure for prediction confidence. Gaining physicians' confidence and faith in DL-based solutions requires knowing how confident a DNN model is in a computer-aided diagnostic model. This project addresses this problem by presenting three distinct strategies for assessing skin cancer detection uncertainty using photos. It also uses innovative uncertainty-related measures to assess and compare the performance of various DNNs. The findings show that predictive uncertainty estimation approaches can identify dangerous and incorrect predictions with a high uncertainty estimate.

We can predict whether someone has skin cancer or not by looking at thousands of images from other people and let the machine compare and predict.

This can help people in early detection and saving them time and money in future also there would be less congestion in the detection systems hence the numbers of critical patient can arrive much early hence saving there life.

Project Objectives

The cancer is the fastest disease in the world, which is responsible for taking many live every year. With the detection at the right time we can make the consequence less and prevent the spread of the disease. There are many medical method to identify whether the patient is suffering from cancer or not but the problem with them is that they all are expensive, so we need to identify a method to identify and predict cancer using the picture. So that it can be early detect with saving the expense and so that it can be expended on curing cancer. The purpose of the project was to detect the skin cancer by using pictures. We are designing a model which will detect the condition of the cancerous lump from picture that will help the people to diagnose the disease early with respect to the lumps size and scar of the disease .Each algorithm model was designed specifically for the purpose of the prediction of the disease by looking at the pictures. For testing the model right now we are using HAM1000 data-set and the images from that data set. IT contains ten thousand and 15 images with 7 types of skin lesion

Project Implementation Method

This project is based on machine learning and deep learning algorithms that will predict whether a person has a cancer or not. To apply these techniques we will be using Python as Programming language. Python is a high-level, general-purpose programming language that is interpreted. The use of considerable indentation in its design philosophy promotes code readability. Its language elements and object-oriented approach are aimed at assisting programmers in writing clear, logical code for both small and large-scale projects.

Python is ideal for machine learning applications because of its advanced features and libraries such as tensorFlow, Keras, and Sciket Learn, which can be used to implement different algorithms and use those methods for training and testing datasets. The powerful Python environment is also capable of processing large datasets and performing complex calculations.

Benefits of the Project

We can use a machine to compare and forecast whether someone has skin cancer by looking at thousands of photos from other people.

This can assist people in early detection, saving them time and money in the future. There will also be less congestion in the detection systems, allowing for the arrival of essential patients much earlier, potentially saving their lives.

Technical Details of Final Deliverable

Observation and cleansing of the global data i.e HAM 1000.

Implementation of DN and ML algorithms.

Making the algorithm and implementing with dataset.

Implementation and comparison of ML vs DN algorithm. 

Final, concluding with a research paper which would be discussing in detail what algorithm can be implemented to achieve the highest accuracy and prediction.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com