Algorithms Saving Lives: AI’s Growing Role in Detecting Cancer Early

Skin cancer has become one of the most common and deadly malignancies worldwide in recent years. Skin cancer mortality can be decreased by early detection. Conventional skin cancer screening techniques are costly, time-consuming, uncomfortable, and may spread the disease. Skin cancer can be diagnosed noninvasively by dermoscopy. The detection of diseases is greatly aided by artificial intelligence (AI), particularly in the field of biomedical engineering. AI-based automated detection systems can increase the diagnostic rate of skin cancer while lowering the problems associated with conventional techniques.

Early Cancer detection through AI

Skin cancer has become one of the most prevalent and widely dispersed malignancies worldwide in recent decades. To lower mortality, early detection of skin cancer is crucial. [1]

Skin cancer types are categorized based on the kind of cells that develop the malignancy. The most prevalent forms of skin cancer are melanoma, basal, and squamous variations.[2]

Skin cancer can be detected in a variety of ways. There are a number of difficulties and restrictions with traditional skin cancer screening techniques like BIOPSY and naked eye (visual assessment by dermatologists or general practitioners).[3]

Automated skin cancer detection system using dermoscopic images to identify benign and malignant skin lesions using AI.

Support vector machine (SVM) and Artificial Neural networks (ANN) -based automated classification techniques are presented and contrasted with one another. To increase classification accuracy, an ANN algorithm is used to determine the most discriminative features for the classification of benign and malignant skin lesions. The following measures are used to assess system efficiency: F1-score, recall, accuracy, specificity, and precision.

Success Stories: AI in Detecting Skin Cancers

An automated skin melanoma diagnosis method using a melanoma-index based on entropy properties was proposed by Kang Hao Cheong et al.

Computer-aided diagnostic and detection techniques were created by Arora et al to categorize lesions as either cancerous or non-cancerous.

 A hybrid quantum mechanical system was created by Lyer et al. to distinguish between pigmented skin lesions that are malignant and those that are not.

The suggested system uses a number of processes, such as pre-processing, various segmentation techniques, features extraction/selection, and classification techniques to analyze the automated dermoscopic pictures in order to identify benign and malignant skin lesions.

Benefits of Using AI

• Faster and more consistent diagnosis.

• Reduction in human diagnostic error.

• Potential cost savings in healthcare by catching cancers before they progress.

  • Democratization of healthcare: remote or underserved areas benefit from AI-assisted screening tools.

    
    
    
    
    
    
    
    

    

    Benefits of AI in Cancer diagnosis

  Limitations

• Data privacy and algorithm transparency ("black box" issue).

• Bias in training data — risk of misdiagnosis in underrepresented populations.

• Regulatory and legal hurdles (e.g., FDA approval, liability).

• Need for human oversight — AI should augment, not replace, clinicians.

  
  

  Conclusion

  
  

  In addition to potentially saving lives, early identification and diagnosis of skin cancer can lower treatment costs overall and result in better treatment outcomes. After receiving the necessary approval and declarations, the suggested system can be used in a real-time diagnosis application in further work.

Source

1. Apalla, Z., Nashan, D., Weller, R. B. & Castellsagué, X. Skin cancer: Epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches. Dermatol. Ther. (Heidelb). 7(Suppl 1), 5–19 (2017).

2. Jones, O. T., Ranmuthu, C. K. I., Hall, P. N., Funston, G. & Walter, F. M. Recognising skin cancer in primary care. Adv. Ther. 37(1), 603–616 (2020).

3. Sreedhar, B., Swamy, M., Kumar, S. A comparative study of melanoma skin cancer detection in traditional and current image processing techniques. In Fourth International Conference on I-SMAC 654–658 (2020).

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