The aim of this assignment is to build a CNN based model which can accurately detect melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution that can evaluate images and alert dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

• Problem Statement

To build a CNN based model which can accurately detect melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution that can evaluate images and alert dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

The dataset consists of 2357 images of malignant and benign oncological diseases, which were formed from the International Skin Imaging Collaboration (ISIC). All images were sorted according to the classification taken with ISIC, and all subsets were divided into the same number of images, with the exception of melanomas and moles, whose images are slightly dominant.

The data set contains the following diseases:

• Actinic keratosis
• Basal cell carcinoma
• Dermatofibroma
• Melanoma
• Nevus
• Pigmented benign keratosis
• Seborrheic keratosis
• Squamous cell carcinoma
• Vascular lesion

• Buisiness Objectives

• A solution which can evaluate images and alert the dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

• Scope of the Case study :

  • A solution which can evaluate images and alert the dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

• Step 1: Loading and Understanding the Data
• Step 2: Data Analysis and Data Cleaning
• Step 3: Data Modelling
• Step 4: Model Evaluation

• Key attributes which which affecting the House Pricing are:

• Business Objectives

• A solution which can evaluate images and alert the dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

• Detailed conclusion mentioned in the Notebook file

• numpy - 1.24.1
• pandas - 1.3.4
• matplotlib.pyplot - 3.5.2
• seaborn - 0.12.2
• statmodels - 0.13.5
• sklearn - 0.0.post1
• tensorflow 2.12.0
• tensorflow-datasets 4.9.2
• tensorflow-estimator 2.12.0
• tensorflow-gcs-config 2.12.0
• tensorflow-hub 0.13.0
• tensorflow-io-gcs-filesystem 0.32.0
• tensorflow-metadata 1.13.1
• tensorflow-probability 0.20.1
• tensorstore 0.1.38

• This project perfored as part of the IIT-B /Upgrad EDG Program

Created by Joshy PJ