Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.

Ag and Cu powders were mechanically alloyed using high-energy planetary milling to evaluate the sinter-bonding characteristics of a die-attach paste containing particles of these two representative conductive metals mixed at atomic scale. This resulted in the formation of completely alloyed Ag-40Cu particles of 9.5 µm average size after 3 h. The alloyed particles exhibited antioxidation properties during heating to 225°C in air; the combination of high pressure and long bonding time at 225°C enhanced the shear strength of the chip bonded using the particles. Consequently, the chips sinter-bonded at 225°C and 10 MPa for 10 min exhibited a sufficient strength of 15.3 MPa. However, an increase in bonding temperature to 250°C was detrimental to the strength, due to excessive oxidation of the alloyed particles. The mechanically alloyed phase in the particle began to decompose into nanoscale Ag and Cu phases above a bonding temperature of 225°C during heating.