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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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A 3D Organotypic Melanoma Spheroid Skin Model
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An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.

Asghar Ali Shah1, Ayesha Sher Ali Shaker2, Sohail Jabbar3

  • 1Department of Computer Science, Bahria University, Islamabad, Pakistan.

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|December 14, 2023
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A new deep learning model effectively detects cutaneous melanoma mutations for early diagnosis. This approach integrates multiple recurrent neural network architectures, significantly improving diagnostic accuracy and aiding treatment efficacy evaluation.

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Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Dermatology

Background:

  • Cutaneous melanoma, a deadly skin cancer, arises from melanocyte mutations, influenced by genetics and environmental factors.
  • Early detection is crucial for improving melanoma survival rates.
  • Current computer-aided diagnosis systems lack satisfactory accuracy, and medical imaging suffers from a shortage of labeled data, necessitating generalized classifiers.

Purpose of the Study:

  • To propose a novel blending ensemble-based deep learning (BEDLM-CMS) model for detecting cutaneous melanoma mutations.
  • To integrate Long Short-Term Memory (LSTM), Bi-directional LSTM (BLSTM), and Gated Recurrent Unit (GRU) architectures for enhanced diagnostic performance.
  • To address the limitations of existing systems, including accuracy issues and data scarcity.

Main Methods:

  • Utilized a dataset comprising 2608 human samples and 6778 mutations across 75 genes, focusing on prominent biomarker genes.
  • Employed multiple feature extraction techniques to identify significant genetic markers.
  • Applied deep learning models optimized via grid search, including LSTM, BLSTM, and GRU, integrated into the BEDLM-CMS framework.

Main Results:

  • The BEDLM-CMS model achieved high accuracy rates: 97% on the independent set test, 94% on the self-consistency test, and 93% on tenfold cross-validation.
  • Individual architectures showed strong performance: BLSTM (99% independent set, 98% self-consistency, 93% tenfold cross-validation), LSTM (96% self-consistency, 94% independent set, 92% tenfold cross-validation), and GRU (94% independent set, 93% self-consistency, 91% tenfold cross-validation).
  • The model demonstrated robustness validated through tenfold cross-validation, independent set testing, and self-consistency testing using metrics like accuracy, specificity, and sensitivity.

Conclusions:

  • The proposed BEDLM-CMS model demonstrates significant potential for the early diagnosis of cutaneous melanoma.
  • The integration of LSTM, BLSTM, and GRU architectures offers a powerful approach to overcome current diagnostic challenges.
  • The findings support the BEDLM-CMS model's effectiveness in early diagnosis and evaluating treatment efficacy for cutaneous melanoma.