Assignment 3 Project Execution, Data Collection and Analysis

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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 &

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4 Viriri, 2020) . During the whole process of data gathering, the research team will be responsible for adhering to the legislation and norms about data protection. Data Analysis The process of analyzing the data is an essential component of this undertaking. After the data has been gathered, it will be preprocessed and used to train the deep learning algorithms. The preprocessed pictures will be fed into a deep-learning model during training. The model parameters will be adjusted to make the gap between the predicted and actual labels as small as possible ( Göçeri, 2020) . Training the model to diagnose skin disorders will continue until the model reaches an acceptable degree of accuracy. After the deep learning model has been trained, it will be assessed using a distinct group of pictures that has not previously been shown. Through this evaluation, we will be able to establish the level of accuracy that the model possesses and whether or not it is appropriate for diagnosing skin disorders. The assessment will also help find areas where the model may be incorrect or biased, enabling the research team to fine-tune the model further. The evaluation will also assist in identifying any areas where the model may be erroneous or biased. Convolutional neural networks, often known as CNNs, are a type of deep learning algorithm beneficial for evaluating pictures. These networks will be utilized to carry out the deep learning analysis for this project. CNNs are built with the capability to learn and extract significant characteristics from pictures automatically. These features are then used to produce predictions about the images. The convolutional neural networks (CNNs) utilized for this research will first undergo pre-training using a sizable picture dataset. It will enable the CNNs to learn characteristics pertinent to skin disorders.

5 In addition to determining how accurate the deep learning model is, the research team will investigate whether or not there is a link between the demographic data and the accuracy of the diagnosis. The accuracy of the model's diagnoses will be compared depending on demographic parameters such as age, sex, and race as part of this investigation. The analysis will assist in evaluating whether the deep learning system is biased toward particular demographics and whether any more fine-tuning is required to increase its accuracy for all patients. It will be determined by determining whether the system is biased toward specific demographics. The research team will also conduct its analysis using various statistical methods to uncover any patterns or trends in the data. The study team will be able to identify, with the help of these methods, which skin disorders can be detected most precisely by the deep learning system and which aspects of the photographs are the most important when making a diagnosis. The findings of this investigation will be used to enhance the precision of the deep learning system and to direct further investigation into this subject area. Results Creating an automated system that can diagnose skin illnesses via deep learning strategies is the central goal of this research. Psoriasis, eczema, melanoma, and acne will all be included in the training that the system receives from a vast collection of preprocessed photos of various skin disorders. This list is meant to be partial. The use of deep learning algorithms will make it possible for the system to recognize minor variations in the photos that human observers could be prone to missing, resulting in increased diagnostic precision. The correctness of the deep learning system will be assessed using a distinct collection of photos that the system has yet to see previously to ensure that the evaluation is accurate. The

6 results of this study will shed light on the correctness of the system and any areas in which it may have inaccuracies or biases. The findings of this evaluation will serve as a guide for subsequent adjustments to the system of deep learning. The study team will, in addition to examining the precision of the system, investigate any connections that may exist between demographic data and the precision of the diagnosis. The study team will be able to identify, with the help of this information, whether the deep learning system is biased toward specific demographics and whether or not any more fine-tuning is required to increase the system's accuracy for all patients ( Reshma et al., 2022) . The completion of this study will yield noteworthy findings for several different reasons. To begin, an automated system that uses deep learning techniques to diagnose skin disorders has the potential to dramatically enhance both the accuracy of the diagnosis and the speed with which it is delivered, which in turn would improve patient results. Second, this study's findings will contribute to the expanding body of research on applying deep learning methods in the medical field ( Bajwa et al., 2020) . This discovery will benefit dermatologists, who must frequently contend with the difficulty of effectively detecting various skin disorders. Last but not least, this study's findings will contribute, in a broader sense, to the fields of artificial intelligence and machine learning. Creating a deep learning system that is accurate and reliable for identifying skin illnesses will give vital information into the capabilities and limits of these methods. This knowledge will be beneficial for future studies on the application of machine learning in several domains, including healthcare.

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7 Conclusion 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. Approximately one-fourth of the global population is afflicted with skin diseases at any given time, making them a significant public health concern. A timely and accurate diagnosis is essential to treat and manage these conditions effectively. However, the conventional technique for diagnosing skin diseases involves a subjective and error-prone visual examination. Automated diagnosis using deep learning algorithms has the potential to substantially improve diagnostic accuracy and efficiency, resulting in improved patient outcomes and more efficient use of healthcare resources. The proposed solution entails developing an automated system that can accurately diagnose various skin conditions, including psoriasis, dermatitis, melanoma, and acne. 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 system's accuracy will be evaluated using a distinct set of images, and correlations between demographic data and diagnosis accuracy will be analyzed. This project's findings will contribute to expanding research on applying deep learning techniques in healthcare, specifically dermatology.

8 References Adegun, A. A., & Viriri, S. (2020). FCN-based DenseNet framework for automated detection and classification of skin lesions in dermoscopy images. IEEE Access , 8 , 150377-150396. Bajwa, M. N., Muta, K., Malik, M. I., Siddiqui, S. A., Braun, S. A., Homey, B., ... & Ahmed, S. (2020). Computer-aided diagnosis of skin diseases using deep neural networks. Applied Sciences , 10 (7), 2488. Burlina, P. M., Joshi, N. J., Ng, E., Billings, S. D., Rebman, A. W., & Aucott, J. N. (2019). Automated detection of erythema migrans and other confounding skin lesions via deep learning. Computers in biology and medicine , pp. 105 , 151–156. Göçeri, E. (2020, November). Impact of deep learning and smartphone technologies in dermatology: Automated diagnosis. In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE. Reshma, G., Al-Atroshi, C., Nassa, V. K., Geetha, B. T., Sunitha, G., Galety, M. G., & Neelakandan, S. (2022). Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images. Intelligent Automation & Soft Computing , 31 (1). Wu, H., Yin, H., Chen, H., Sun, M., Liu, X., Yu, Y., ... & Lu, Q. (2020). A deep learning, image-based approach for automated diagnosis of inflammatory skin diseases. Annals of translational medicine , 8 (9).

Assignment 3 Project Execution, Data Collection and Analysis

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