Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation

Cancer is the leading cause of mortality in most countries, with the World Health Organization estimating it is the primary or secondary cause of death in 112 of 183 countries for individuals less than 70 years old. This brief evaluates the diagnosis of four types of common cancers, breast, lung, oral, and skin, with different state-of-the-art feature-based transfer learning models. The findings are expected to be insightful to various stakeholders in the diagnosis of cancer.

Format: Paperback / softback
Length: 34 pages
Publication date: 19 January 2023
Publisher: Springer Verlag, Singapore

Cancer is a devastating disease that has become the leading cause of mortality in most, if not all, countries worldwide. According to the World Health Organization (WHO), cancer was responsible for the deaths of over 9.6 million people in 2018 alone. This alarming statistic highlights the urgent need for effective cancer prevention, diagnosis, and treatment strategies.

One of the most significant challenges in cancer diagnosis is the lack of standardized and accurate methods. Traditional diagnostic techniques, such as biopsies and imaging scans, can be subjective and may lead to misdiagnosis. Moreover, the development of new cancer types and the evolution of existing ones make it difficult for healthcare professionals to keep up with the latest diagnostic guidelines.

Fortunately, with the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is becoming more feasible. Feature-based transfer learning models, which utilize machine learning algorithms to analyze medical images and identify patterns, have shown promising results in cancer diagnosis. These models can help healthcare professionals identify cancerous cells and distinguish them from healthy cells, leading to more accurate and timely diagnoses.

In this brief, we will evaluate the diagnosis of four types of common cancers: breast, lung, oral, and skin cancer. We will discuss the state-of-the-art feature-based transfer learning models that have been developed for these cancers and their potential applications in clinical practice.

Breast cancer is one of the most prevalent cancers worldwide, with an estimated 2.2 million new cases diagnosed each year. Early detection is crucial for successful treatment, and mammography is the primary screening tool for breast cancer. However, mammography can be subjective and may miss certain types of breast cancer.

Feature-based transfer learning models have been used to improve the accuracy of mammography in identifying breast cancer. One such model is Deep Learning-based Mammography (DLBM), which uses convolutional neural networks to analyze mammographic images and detect breast cancer. DLBM has shown promising results in identifying breast cancer with high sensitivity and specificity, even in cases where mammography is inconclusive.

Lung cancer is another major cause of cancer-related deaths worldwide, with an estimated 2.2 million new cases diagnosed each year. Lung cancer is difficult to diagnose early due to its slow growth and lack of specific symptoms. However, imaging scans, such as CT scans and PET scans, can be used to detect lung cancer at an early stage.

Feature-based transfer learning models have also been used to improve the accuracy of lung cancer diagnosis. One such model is Deep Convolutional Neural Networks (DCNNs), which have been trained on large datasets of lung cancer images to identify cancerous cells and distinguish them from healthy cells. DCNNs have shown promising results in identifying lung cancer with high sensitivity and specificity, even in cases where imaging scans are inconclusive.

Oral cancer is a less common type of cancer, but it can be devastating if not diagnosed and treated early. Oral cancer is often caused by tobacco use and alcohol consumption, and it can affect the lips, tongue, cheeks, and throat.

Feature-based transfer learning models have been used to improve the accuracy of oral cancer diagnosis. One such model is Convolutional Neural Networks (CNNs), which have been trained on large datasets of oral cancer images to identify cancerous cells and distinguish them from healthy cells. CNNs have shown promising results in identifying oral cancer with high sensitivity and specificity, even in cases where imaging scans are inconclusive.

Skin cancer is the most common type of cancer in the United States, with an estimated 1 million new cases diagnosed each year. Skin cancer can occur on any part of the body, and it is often caused by exposure to ultraviolet (UV) radiation from the sun.

Feature-based transfer learning models have been used to improve the accuracy of skin cancer diagnosis. One such model is Deep Learning-based Skin Cancer Detection (DLSCD), which uses convolutional neural networks to analyze skin images and detect skin cancer. DLSCD has shown promising results in identifying skin cancer with high sensitivity and specificity, even in cases where imaging scans are inconclusive.

In conclusion, cancer is a complex and challenging disease that requires effective diagnosis and treatment strategies. Feature-based transfer learning models have shown promising results in improving the accuracy of cancer diagnosis, and they have the potential to revolutionize the way we approach cancer care. As technology continues to advance, we can expect to see more innovative and effective approaches to cancer diagnosis and treatment in the future.

Weight: 93g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811989360
Edition number: 1st ed. 2023