Treffer: Convolutional Neural Network Approach for Early Skin Cancer Detection.

The field of medical image processing is rapidly adopting artificial intelligence. Its use is required for many applications in the healthcare industry. A machine can learn from experience without explicit programming thanks to computer education. It is an area within AI. Deep learning, a kind of machine learning, infers critical features for image processing via multiple layer processing and mathematical operations based on artificial neural networks. In the field of healthcare, which encompasses medicine and dentistry, artificial intelligence has several applications. Early melanoma skin cancer identification is necessary for effective therapy. Melanoma, among the various types of skin cancer, has recently gained international recognition as the most deadly one since it is much more likely to spread to other body regions if detected and treated quickly. Clinical diagnosis of various ailments is increasingly using non-invasive medical computer vision or medical image processing. These methods offer an automatic image processing tool that makes it possible to examine the lesion quickly and precisely. The procedures used in this study included building a database of dermoscopy images, preprocessing, segmenting using thresholding, extracting statistical features using asymmetry, border, colour, diameter, etc., and choosing features based on the total dermoscopy score, principal component analysis (PCA), and convocation neural network classification (CNN). According to the findings, a classification accuracy of 90.1% was attained. [ABSTRACT FROM AUTHOR]

المقال يركز على تطبيق الشبكات العصبية التلافيفية (CNNs) للكشف المبكر عن سرطان الجلد، وخاصة الميلانوما، التي تُعتبر واحدة من أكثر أشكال سرطان الجلد فتكًا. يناقش المقال دمج الذكاء الاصطناعي في معالجة الصور الطبية، مؤكدًا على أهمية التشخيص المبكر للعلاج الفعال. تتناول الدراسة منهجية بناء قاعدة بيانات صور الديرموسكوبي، والمعالجة المسبقة، واستخراج الميزات، والتصنيف باستخدام هياكل مختلفة من الشبكات العصبية التلافيفية، محققة دقة تصنيف تصل إلى 90.1%. تسلط النتائج الضوء على إمكانيات الشبكات العصبية التلافيفية في تعزيز دقة وكفاءة الكشف عن سرطان الجلد، مما يبرز الحاجة إلى تقنيات حسابية متقدمة في الرعاية الصحية. [Extracted from the article]

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