Meet SOTONWA KEHINDE ADEBOLA, an Academic Staff of Lagos State University.


Data Science, Ai, Software Engineering, Software Complexity, Cyber Security


Lecturer I


Computer Science


At the Computer Science department office

Visiting Hour

Appointment on Visitation important

Research Interest

Topic: Data Analytics Tools For Software Complexity Metrics: A Comparative Study

Description: Data science is an essential part of the technology industry revived due to an increase in computing power, the presence of huge amounts of data, and a better understanding of techniques in the area of data analytics, artificial intelligence, machine learning and deep learning for solving many challenging problems. In the search for a good programming language on which many data science applications can be developed, the need to develop quality and cost-effective software cannot be overemphasized. Hence, there arises the need to apply code-based metrics to three different data analytical tools Python, R, and Scala to evaluate the complexities of different implementations of the quicksort algorithm and measure the degree of relationship among them. It was discovered that Scala is realized to be the most composite tool for all the metrics while Python and R are average at the same level.


# Certificate SchoolYear
1. Ph.D (Computer Science) Computer Engineering/ Ladoke Akintola University of Technology, Ogbomoso, Oyo State 2020

Current Research

Development of deep learning model in breast cancer using digital pathology

Research Details

There's a surge of interest in using deep learning, a type of artificial intelligence, to analyze digital pathology images for breast cancer detection and diagnosis. This fascination stems from the potential of deep learning models to:

  • Improve accuracy and efficiency: Deep learning can automatically identify patterns in complex images that may be missed by the human eye, potentially leading to earlier and more precise diagnoses.
  • Assist pathologists: Models can act as a second opinion for pathologists, highlighting suspicious areas and reducing workload.
  • Develop personalized medicine: By analyzing tumor characteristics, deep learning could aid in predicting a cancer's aggressiveness and guide personalized treatment plans.

Researchers are actively developing deep learning models for various breast cancer tasks. Some areas of focus include:

  • Classification: Distinguishing between benign and malignant tumors.
  • Grading: Assessing the severity of cancer based on factors like cell growth.
  • Subtyping: Identifying specific breast cancer subtypes for targeted therapies.

Overall, deep learning in digital pathology holds promise for revolutionizing breast cancer diagnosis and treatment. However, challenges like data privacy and ensuring model fairness require ongoing research.


SOTONWA KEHINDE is a Lecturer I at the Department of Computer Science

SOTONWA has a Ph.D in Computer Science from Computer Engineering/ Ladoke Akintola University of Technology, Ogbomoso, Oyo State

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