Model

Data Source

The dataset was taken from the BCCD dataset. The image folder consists of three subfolders - TRAIN, TEST_SIMPLE, and TEST. Each of the three folders is divided into four categories - LYMPHOCYTE, MONOCYTE, NEUTROPHIL, and EOSINOPHIL. The images in the TRAIN and TEST folders are augmented.

def mask (path) :

    

    x = cv2.resize(cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB), (128,128))

    m = np.ones(x.shape)*255.0

    

    m[(x[:,:,0] >= 160) & (x[:,:,1] >= 140) & (x[:,:,2] >= 140)] = [0.0,0.0,0.0]

    

    kernel = np.ones((3,3), dtype = np.uint8)

    m = cv2.dilate(m, kernel, iterations = 2)

    

    return cv2.bitwise_and(x, np.array(m, np.uint8))

Output images

Model

The model is a Sequential network of Convolution and Fully connected layers. Dropout of 0.4 is added to the fully connected layer. And weight regularization is added to keep the weights small and generalize the model better. In addition, four convolutional layers were added with a 3x3 kernel size and strides of 1. And a max-pooling layer was added to reduce the image dimensions.

def conv_layer (filters) :

    

    model = Sequential()

    

    model.add(Conv2D(filters,

                     (3,3),

                     strides = 1,

                     padding = 'same',

                     kernel_regularizer = 'l2'))

    

    model.add(BatchNormalization())

    model.add(LeakyReLU())

    model.add(MaxPooling2D((2, 2)))

    

    return model

def dens_layer (hiddenx) :

    

    model = Sequential()

    

    model.add(Dense(hiddenx,

                    kernel_regularizer = 'l2'))

    

    model.add(BatchNormalization())

    model.add(Dropout(.4))

    model.add(LeakyReLU())

    

    return model

def cnn (filter1,

         filter2,

         filter3,

         filter4,  hidden1) :

    

    model = Sequential([

        

        Input((128,128,3,)),

        conv_layer(filter1),

        conv_layer(filter2),

        conv_layer(filter3),

        conv_layer(filter4),

        Flatten(),

        dens_layer(hidden1),

        Dense(4, activation = 'softmax')

    ])

    

    model.compile(loss = 'categorical_crossentropy', optimizer = Adam(learning_rate = 0.0001),

                  metrics = ['accuracy'])

    return model

Performance

Validation data



Testing data