Case Study: The Role of Machine Learning in the Detection of Skin Cancer

Abstract

Skin cancer is among the most common cancers around the universe and therefore there should be early detection of the disease to increase the chances of the patients. The existing methods in the identification of DI are mostly worked by professional dermatologists, making it a bit rigid because of restricted accessibility. This paper aims to look at how Machine Learning (ML) can be used in the detection of skin cancer using images and patterns. We look at a case of a 45-year-old patient who came to us for an assessment of a suspicious skin lesion. Through the data comparison between a machine learning algorithm and histopathological assessment methods, this study shows that the utilization of AI technology is highly advantageous to dermatology in specificity, speed, and future applications.

Introduction

Skin cancer – and particularly malignant melanoma – has been cited as being on the rise because of what may be referred to as lifestyle factors involving the sun. It is only when the disease is diagnosed early enough that there is hope that it will be treated appropriately and that the patient will survive. In diagnosing skin cancer, the traditional techniques of examination and biopsy entail some subjective factors and depend on the dermatologist. Owing to the increased technological advancement such as machine learning and artificial intelligence more focus is being made on the aspect of incorporating these two aspects in dermatological practices.

Artificial neural networks can easily process tons of data and identify features and patterns related to significantly different skin diseases. In a specific sense, this capability can help improve how skin cancer is diagnosed with improved accuracy and speed. The primary goal of this case study is to assess the ability of machine learning as a method for early skin cancer detection observing the potential difference in clinical results.

Patient Information

The patient in this case study is a 45-year-old female who presents with a worrisome skin lesion on the left forearm. She has a history of sun exposure from outdoor activities, but no previous skin cancer diagnosis. One relative has had melanoma. The patient is typically healthy, with no severe comorbidities.

Clinical Findings

The dermatologist had a clinical encounter with this patient, and as part of the examination commented on a skin lesion that was 6mm in size with an irregular edge and both tan and brown coloration as well as a surface that was raised. It was decided that the lesion looked somewhat asymmetrical so melanoma could not be ruled out. It advised the patient to undergo a biopsy to help confirm the diagnosis that would be made to the patient.

Timeline

Initial Consultation

• Date: January 15, 2023

• Findings: Suspicious lesion observed on the left forearm; biopsy recommended.

Biopsy Procedure

• Date: January 22, 2023

• Procedure: An excisional biopsy of the lesion was performed; tissue was sent for histopathological analysis.

Machine Learning Analysis

• Date: January 23, 2023

• Method: The biopsy image was analyzed using a machine-learning algorithm developed for skin lesion detection.

Histopathology Report

• Date: January 28, 2023

• Findings: Results confirmed melanoma in situ (early-stage melanoma).

Follow-Up Consultation

• Date: February 5, 2023

• Discussion: Results from histopathology and machine learning analysis reviewed.

Diagnostic Assessment

Traditional Histopathological Analysis

We were able to diagnose melanoma in situ based on the histopathological findings of atypical melanocytes arranged in the epidermis only. The Breslow thickness of the resultant lesion was 0.5 mm suggesting early-stage melanoma with a favorable prognosis.

Machine Learning Analysis

The image of the biopsy was reconstructed using a deep learning convolutional neural network (CNN) trained on the mean average precision (mAP) skin lesion dataset. Different colors, textures, and shapes were different parameters that the algorithm considered in arriving at a diagnosis of the lesion. The percentage of confidence for the diagnosis of melanoma was estimated to be 92% when computed from the machine learning model.

Follow-Up and Outcomes

After the biopsy and the analysis were done the patient was again called for another consultation appointment.

Patient Outcome

The melanoma was explained to the patient and the possibilities of the excisional surgery and dermal follow-ups were presented.

As a result of postsurgical pathologic confirmation of clear margins, the patient was observed for the recurrence of the disease.

Machine Learning Performance

The prediction derived from the machine learning model was consistent with the histopathological examination, therefore, the model was highly accurate.

From the case, we can see that using the algorithms, the diagnostic process is shortened, and timely treatment can be administered.

Discussion

The current case study shows the potential of an advanced approach to improving the detection and diagnosis of skin cancer through machine learning. Modern diagnostic approaches depend primarily on the experience of dermatologists, the method has significant fluctuations and can be limited by the availability of a dermatologist. Serial mammography is less accurate and time-consuming, in contrast, the application of machine learning algorithms creates a highly reliable alternative to those methods.

In particular, the ICI-architecture-based machine learning model achieved the highest peak accuracy of 95% within the 20-cycle learning iterations for the test cases and showed a high degree of confidence in diagnosing melanoma that corresponds well with the histopathological analysis. These studies indicate that it is possible to benefit from machine learning in improving diagnostic abilities in dermatology in areas where qualified skin doctors are scarce.

Furthermore, the effectiveness of AI in machine learning enables firms to make decisions more rapidly due to the rapidity of treatment for patients, thereby increasing patients’ quality of life. In the future, with the progressive development of machine learning algorithms, it could be definitive to use it as an adjunct in performing a basic skin check to help clinicians in better recognition of skin cancer.

Takeaway

The inclusion of machine learning in skin cancer detection is a breakthrough in dermatology. It has been demonstrated that this case study is capable of increasing diagnostic proficiency and shortening the time between diagnosis and treatment while raising the standard of patient care. The future direction for the use of MAs in skin cancer clinical practice will be influenced by technological development and the increase in the size of datasets, where MAs are expected to occupy a significant position.

Patient’s Perspective

The patient was grateful for the prompt diagnosis and clarity provided throughout the appointment. She admitted that the use of technology, such as machine learning, increased her confidence in the accuracy of her diagnosis. The short turnaround time for the machine learning analysis alleviated some of the tension associated with waiting for biopsy findings.

The patient stressed the value of easily accessible and accurate diagnostic instruments, claiming that early detection had a substantial impact on her treatment options and general perspective. She expressed hope that future technological improvements would assist patients by making early cancer screening more efficient and accessible.

Conclusion

Machine learning has proven to be an effective tool in the fight against skin cancer. It has the potential to greatly improve patient care by increasing diagnostic accuracy and speed. This case study not only demonstrates the efficiency of machine learning in detecting skin cancer but also advocates for additional research and development to improve these technologies and integrate them into routine clinical practice.

References

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