Assignment 3 Project Execution, Data Collection and Analysis

.docx

School

Arizona State University *

*We aren’t endorsed by this school

Course

309

Subject

Computer Science

Date

Dec 6, 2023

Type

docx

Pages

8

Uploaded by AdmiralKnowledge12905

Report
1 Assignment 3: Project Execution, Data Collection and Analysis Vidhiben Rasikbhai Gajjar 19 th April 2023 ITM 6000 Webster University
2 Automated Diagnosis of Skin Diseases Using Deep Learning Introduction Many individuals worldwide suffer from skin diseases, and prompt and accurate diagnosis is essential for effectively treating and managing these conditions. Nevertheless, visual examination of skin diseases is subjective and prone to error. To address this issue, the proposed project, "Automated Diagnosis of Skin Diseases Using Deep Learning," seeks to develop an automated system capable of accurately diagnosing skin diseases using deep learning techniques. The system will be trained on a large dataset of preprocessed images of skin disorders. The deep learning algorithms will enable it to detect subtle differences in the images that human observers might overlook. The proposed solution has the potential to substantially improve the accuracy and efficiency of diagnosing skin diseases, resulting in improved patient outcomes and more efficient utilization of healthcare resources. This project's findings will contribute to the increasing corpus of research on using deep learning techniques in healthcare. They will have significant implications for dermatology and the broader fields of artificial intelligence and machine learning. Data Collection The method of gathering information is an essential component of this endeavor. For the system to effectively detect skin disorders, it must first be trained using a dataset of photographs depicting a variety of skin ailments. It will enable the system to recognize minor variations in the images. The procedure of gathering information for this research entails collecting photographs
3 of various skin disorders from various sources, such as Internet databases, medical institutes, and private practitioners. Psoriasis, eczema, melanoma, and acne are some of the skin disorders that will be included in the collection, comprising several thousand photos in total. The photographs will each be accompanied by a caption describing the particular skin disease it depicts ( Burlina et al., 2019) . Because it will enable the deep learning algorithms to understand the patterns and features of each skin disease, this labeling will be vital for the training process. Preprocessing will be performed on the photos before they are used for training to guarantee that they all have the exact dimensions and are in the same format. In addition, during the preprocessing step, any artifacts or noise in the pictures that can impact the training process's precision will be eliminated ( Wu et al., 2020) . After preprocessing the photos, they will be separated into training, validation, and test sets. The vast majority of the images are utilized for training deep learning algorithms. In addition to the photographs, demographic information, such as the ages, sexes, and races of the patients who volunteered to take their photographs, will also be gathered. This data will be used to investigate whether there is a link between the demographic information and the accuracy of the diagnosis provided by the deep learning system. The data will be anonymized to safeguard the patient's right to confidentiality. The method of collecting data will adhere to ethical norms, which will guarantee that the patient's privacy will be preserved and that the data will be gathered in a legal and moral way. The patients will be asked for their informed consent before the study team collects their photos and demographic data; this will ensure their confidentiality is maintained ( Adegun &
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help