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Skin lesion classification in dermoscopic images using stacked Convolutional Neural Network

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Abstract

Skin lesion detection and classification is always observed as a difficult problem to solve. Manual detection of skin lesions via visual image inspection can be time-consuming and tedious. Automatic diagnosis and classification are considered a critical problem to solve because of the involvement of many factors like different image sizes, hairs in the image, bad color schemes, ruler marker, low-contrast, variation in lesion sizes, and gel bubble. Different methodologies were proposed by the researchers in the Dermatology Pigmented lesion classification. Researchers work on the binary class problem for the detection of Melanocytic lesions from the normal one. This study makes use of the MNIST HAM10000 dataset published by International Skin Image Collaboration. The dataset consists of seven classes of skin cancer diseases. Furthermore in this research, our stacked CNN model proves its superiority by achieving 95.2% accuracy along with data augmentation and image preprocessing techniques.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to Third Party Involvement (Kaggle) for the generation of the dataset. The dataset is available from the corresponding author on reasonable request.

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Funding

The funding agency had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. This research was supported by National Research Foundation of Korea (Grant NRF-2019R1A2C1006159).

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Authors and Affiliations

  1. Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

    Ahmad Hameed, Umair Hafeez, Hassan Mustafa, Ahmed Sohaib & Hamza Ahmad Madni

  2. Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

    Muhammad Umer & Muhammad Abubakar Siddique

  3. Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Umer

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  1. Ahmad Hameed
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  2. Muhammad Umer
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  3. Umair Hafeez
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  4. Hassan Mustafa
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  5. Ahmed Sohaib
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  6. Muhammad Abubakar Siddique
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  7. Hamza Ahmad Madni
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Correspondence to Muhammad Umer, Ahmed Sohaib or Hamza Ahmad Madni.

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Hameed, A., Umer, M., Hafeez, U. et al. Skin lesion classification in dermoscopic images using stacked Convolutional Neural Network. J Ambient Intell Human Comput 14, 3551–3565 (2023). https://doi.org/10.1007/s12652-021-03485-2

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